@article{Gneiting2000,
author = {Gneiting, Tilmann},
title = {{Power-law correlations, related models for long-range dependence and their simulation}},
journal = {Journal of Applied Probability},
year = {2000},
volume = {37},
number = {4},
pages = {1104--1109},
month = dec
}

@article{Guttorp2006,
    author = {Guttorp, Peter and Gneiting, Tilmann},
    title = "{Studies in the history of probability and statistics XLIX On the Mat\'ern correlation family}",
    journal = {Biometrika},
    volume = {93},
    number = {4},
    pages = {989-995},
    year = {2006},
    month = {12},
    abstract = "{Handcock \\&amp; Stein (1993) introduced the MatÃ©rn family of spatial correlations into statistics as a flexible parametric class with one parameter determining the smoothness of the paths of the underlying spatial field. We document the varied history of this family, which includes contributions by eminent physical scientists and statisticians.}",
    issn = {0006-3444},
    doi = {10.1093/biomet/93.4.989},
    url = {https://doi.org/10.1093/biomet/93.4.989},
    eprint = {http://oup.prod.sis.lan/biomet/article-pdf/93/4/989/593552/934989.pdf},
}


@book{gnedenko1954limit,
  title={Limit distributions for sums of independent random variables},
  author={Gnedenko, B V and Kolmogorov, A N},
  publisher={Addison-Wesley, Cambridge, Mass. Transl. from Russian by K. L. Chung},
  year={1954}
}


@incollection{porcu2012some,
  title={On some local, global and regularity behaviour of some classes of covariance functions},
  author={Porcu, Emilio and Stein, Michael L},
  booktitle={Advances and challenges in space-time modelling of natural events},
  pages={221--238},
  year={2012},
  publisher={Springer}
}


@article{park2008bayesian,
  title={The {B}ayesian lasso},
  author={Park, Trevor and Casella, George},
  journal={Journal of the American Statistical Association},
  volume={103},
  number={482},
  pages={681--686},
  year={2008},
  publisher={Taylor \& Francis}
}


@article{cressie1992statistics,
  title={Statistics for spatial data},
  author={Cressie, Noel},
  journal={Terra Nova},
  volume={4},
  number={5},
  pages={613--617},
  year={1992},
  publisher={Wiley Online Library}
}

@incollection{neal1996priors,
  title={Priors for infinite networks},
  author={Neal, Radford M},
  booktitle={Bayesian Learning for Neural Networks},
  pages={29--53},
  year={1996},
  publisher={Springer}
}


@book{matern2013spatial,
  title={Spatial variation},
  author={Mat{\'e}rn, Bertil},
  volume={36},
  year={1960},
  publisher={Springer Science \& Business Media}
}

@article{madan08,
title={Representing the {CGMY} and {M}eixner {L}\'evy
processes as time changed {B}rownian motions},
author={Madan, D B and Yor, M},
journal={Journal of Computational Finance},
volume={12},
pages={27--47},
year={2008}
}

@article{bhadra2019horseshoe,
  title={Horseshoe Regularization for Machine Learning in Complex and Deep Models},
  author={Bhadra, Anindya and Datta, Jyotishka and Li, Yunfan and Polson, Nicholas G},
  journal={International Statistical Review},
  volume={to appear with discussion},
  year={2019}
}

@article{Gay1990,
 ISSN = {00063444},
 URL = {http://www.jstor.org/stable/2336820},
 abstract = {In this note it is shown how a convenient class of random field models with rational spectral densities can be augmented to incorporate the possibility of long range dependence.},
 author = {R. Gay and C. C. Heyde},
 journal = {Biometrika},
 number = {2},
 pages = {401--403},
 publisher = {[Oxford University Press, Biometrika Trust]},
 title = {On a Class of Random Field Models which Allows Long Range Dependence},
 volume = {77},
 year = {1990}
}


@article{Beran1992,
author = "Beran, Jan",
doi = "10.1214/ss/1177011122",
fjournal = "Statistical Science",
journal = "Statist. Sci.",
month = "11",
number = "4",
pages = "404--416",
publisher = "The Institute of Mathematical Statistics",
title = "Statistical Methods for Data with Long-Range Dependence",
url = "https://doi.org/10.1214/ss/1177011122",
volume = "7",
year = "1992"
}


@article{Haslett1989,
 ISSN = {00359254, 14679876},
 URL = {http://www.jstor.org/stable/2347679},
 author = {John Haslett and Adrian E. Raftery},
 journal = {Journal of the Royal Statistical Society. Series C (Applied Statistics)},
 number = {1},
 pages = {1--50},
 publisher = {[Wiley, Royal Statistical Society]},
 title = {Space-Time Modelling with Long-Memory Dependence: Assessing Ireland's Wind Power Resource},
 volume = {38},
 year = {1989}
}


@Manual{R,
  title = {R: A Language and Environment for Statistical Computing},
  author = {{R Core Team}},
  organization = {R Foundation for Statistical Computing},
  address = {Vienna, Austria},
  year = {2018},
  url = {https://www.R-project.org/},
}

@Book{Wickham2016,
author = {Hadley Wickham},
title = {ggplot2: Elegant Graphics for Data Analysis},
publisher = {Springer-Verlag New York},
year = {2016},
isbn = {978-3-319-24277-4},
url = {https://ggplot2.tidyverse.org},
}

@article{Stein1987,
author = { Michael   Stein },
title = {Large Sample Properties of Simulations Using Latin Hypercube Sampling},
journal = {Technometrics},
volume = {29},
number = {2},
pages = {143-151},
year  = {1987},
publisher = {Taylor & Francis},
doi = {10.1080/00401706.1987.10488205},

URL = { 
        https://www.tandfonline.com/doi/abs/10.1080/00401706.1987.10488205
    
},
eprint = { 
        https://www.tandfonline.com/doi/pdf/10.1080/00401706.1987.10488205
    
}

}

@book{Ripley1981,
  title={Spatial Statistics},
  author={Ripley, B.D.},
  isbn={9780471083672},
  lccn={80026104},
  series={Wiley Series in Probability and Statistics},
  url={https://books.google.com/books?id=dxvvAAAAMAAJ},
  year={1981},
  publisher={Wiley}
}

@book{Journel1978mining,
author = {Journel, A G and Huijbregts, C J},
title = {{Mining Geostatistics}},
publisher = {Academic Press},
year = {1978}
}

@book{Santner2018,
author = {Santner, Thomas J and Williams, Brian J and Notz, William I},
title = {{The design and analysis of computer experiments; 2nd ed.}},
publisher = {Springer},
year = {2018},
series = {Springer series in statistics},
address = {New York}
} 

@book{Williams2006,
  title={Gaussian processes for machine learning},
  author={Williams, Christopher KI and Rasmussen, Carl Edward},
  year={2006},
  publisher={MIT press Cambridge, MA}
}




@article{Leonenko1991,
author = {Leonenko, N N and Olenko, A Ya},
title = {{Tauberian and Abelian theorems for correlation function of a homogeneous isotropic random field}},
journal = {Ukrainian Mathematical Journal},
year = {1991},
volume = {43},
number = {12},
pages = {1539--1548},
month = dec
}

@book{Leonenko1999,
  title={Limit Theorems for Random Fields with Singular Spectrum},
  author={Leonenko, N.},
  isbn={9780792356356},
  lccn={99018120},
  series={Mathematics and Its Applications},
  url={https://books.google.com/books?id=aPfuVWJpR1wC},
  year={1999},
  publisher={Springer Netherlands}
}


@Inbook{Flandrin2003,
author="Flandrin, Patrick
and Borgnat, Pierre
and Amblard, Pierre-Olivier",
editor="Rangarajan, Govindan
and Ding, Mingzhou",
title="From Stationarity to Self-similarity, and Back: Variations on the Lamperti Transformation",
bookTitle="Processes with Long-Range Correlations: Theory and Applications",
year="2003",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",
pages="88--117",
isbn="978-3-540-44832-7",
doi="10.1007/3-540-44832-2_5",
url="https://doi.org/10.1007/3-540-44832-2_5"
}

@article{Lim2009,
author = {Lim, S C and Teo, L P},
title = {{Gaussian fields and Gaussian sheets with generalized Cauchy covariance structure}},
journal = {Stochastic Processes and their Applications},
year = {2009},
volume = {119},
number = {4},
pages = {1325--1356},
month = apr
}



@article{Kaufman2013,
author = {Kaufman, C G and Shaby, B A},
title = {{The role of the range parameter for estimation and prediction in geostatistics}},
journal = {Biometrika},
year = {2013},
volume = {100},
number = {2},
pages = {473--484},
month = may
}


@article{Stein1988,
author = {Stein, Michael L},
title = {{Asymptotically Efficient Prediction of a Random Field with a Misspecified Covariance Function}},
journal = {The Annals of statistics},
year = {1988},
volume = {16},
number = {1},
pages = {55--63},
month = mar
}

@article{Stein1997,
author = {Stein, Michael L},
title = {{Efficiency of linear predictors for periodic processes using an incorrect covariance function}},
journal = {Journal of Statistical Planning and Inference},
year = {1997},
volume = {58},
number = {2},
pages = {321--331}
}

@article{Stein1989,
author = {Stein, Michael L and Handcock, M S},
title = {{Some asymptotic properties of kriging when the covariance function is misspecified}},
journal = {Mathematical Geology},
year = {1989},
volume = {21},
number = {2},
pages = {171--190}
}



@article{Gu2018,
author = {Gu, Mengyang and Wang, Xiaojing and Berger, James O},
title = {{Robust Gaussian stochastic process emulation}},
journal = {The Annals of statistics},
year = {2018},
volume = {46},
number = {6A},
pages = {3038--3066},
month = dec
}


@article{Zhang2004,
author = {Zhang, Hao},
title = {{Inconsistent Estimation and Asymptotically Equal Interpolations in Model-Based Geostatistics}},
journal = {Journal of the American Statistical Association},
year = {2004},
volume = {99},
number = {465},
pages = {250--261},
month = mar
}

@article{Wang2011,
author = {Wang, Daqing and Loh, Wei-Liem},
title = {{On fixed-domain asymptotics and covariance tapering in Gaussian random field models}},
journal = {Electronic Journal of Statistics},
year = {2011},
volume = {5},
number = {0},
pages = {238--269}
}



@article{Gneiting2002,
author = {Gneiting, Tilmann},
title = {Compactly Supported Correlation Functions},
journal = {Journal of Multivariate Analysis},
year = {2002},
volume = {83},
number = {2},
pages = {493--508},
month = nov
}


@article{Cressie1990,
author = {Cressie, Noel},
title = {{The origins of kriging}},
journal = {Mathematical Geology},
year = {1990},
volume = {22},
number = {3},
pages = {239--252}
}

@article{BERGER2001,
author = {Berger, J O and De Oliveira, V and Sanso, B},
title = {{Objective Bayesian analysis of spatially correlated data}},
journal = {Journal of the American Statistical Association},
year = {2001},
volume = {96},
number = {456},
pages = {1361--1374},
month = dec,
annote = {Class material}
}

@article{Sacks1989,
author = {Sacks, Jerome and Welch, William J and Mitchell, Toby J and Wynn, Henry P},
title = {{Design and Analysis of Computer Experiments}},
journal = {Statistical Science},
year = {1989},
volume = {4},
number = {4},
pages = {409--423},
month = nov
}



@article{Matheron1963,
author = {Matheron, Georges},
title = {{Principles of geostatistics}},
journal = {Economic Geology},
year = {1963},
volume = {58},
number = {8},
pages = {1246--1266}
}



@book{Abramowitz1965,
  title={Handbook of mathematical functions: with formulas, graphs, and mathematical tables},
  author={Abramowitz, Milton and Stegun, Irene A},
  volume={55},
  year={1965},
  publisher={Courier Corporation}
}

@book{journel1978,
author = {Journel, A G and Huijbregts, C J},
title = {{Mining Geostatistics}},
publisher = {Academic Press},
year = {1978}
}


@article{Stein2002,
author = {Stein, Michael L},
title = {{The screening effect in Kriging}},
journal = {The Annals of statistics},
year = {2002},
volume = {30},
number = {1},
pages = {298--323}
}

@article{Stein2011,
author = {Stein, Michael L},
title = {{2010 Rietz lecture: When does the screening effect hold?}},
journal = {The Annals of statistics},
year = {2011},
volume = {39},
number = {6},
pages = {2795--2819},
month = dec
}


@article{Stein2015,
author = {Stein, Michael L},
title = {{When does the screening effect not hold?}},
journal = {Spatial Statistics},
year = {2015},
volume = {11},
pages = {65--80},
month = feb
}



@article{Soni1975,
  title={Slowly varying functions and asymptotic behavior of a class of integral transforms {I}},
  author={Soni, K and Soni, RP},
  journal={Journal of Mathematical Analysis and Applications},
  volume={49},
  number={1},
  pages={166--179},
  year={1975},
  publisher={Academic Press}
}

@techreport{Karamata1962,
  title={Some theorems concerning slowly varying functions},
  author={Karamata, Jovan},
  year={1962},
  institution={WISCONSIN UNIV-MADISON MATHEMATICS RESEARCH CENTER}
}

@article{Gneiting2004,
author = {Gneiting, Tilmann and Schlather, Martin},
title = {{Stochastic Models That Separate Fractal Dimension and the Hurst Effect}},
journal = {SIAM Review},
year = {2004},
volume = {46},
number = {2},
pages = {269--282},
month = jan
}

@article{Stein2005,
author = {Stein, ML},
title = {Nonstationary spatial covariance functions},
journal = {Unpublished Report},
year = {2005}
}



                
@book{Cressie1993,
author = {Cressie, Noel},
title = {{Statistics for Spatial Data}},
publisher = {John Wiley {\&} Sons},
year = {1993},
address = {New York},
edition = {Revised}
}


@article{Efron2011,
author = {Bradley Efron},
title = {Tweedieâs Formula and Selection Bias},
journal = {Journal of the American Statistical Association},
volume = {106},
number = {496},
pages = {1602-1614},
year  = {2011},
publisher = {Taylor & Francis},
doi = {10.1198/jasa.2011.tm11181},

URL = { 
        https://doi.org/10.1198/jasa.2011.tm11181
    
},
eprint = { 
        https://doi.org/10.1198/jasa.2011.tm11181
    
}

}

@book{Adler2010,
author = {Adler, R.},
title = {The Geometry of Random Fields},
publisher = {Society for Industrial and Applied Mathematics},
year = {2010},
doi = {10.1137/1.9780898718980},
address = {},
edition   = {},
URL = {https://epubs.siam.org/doi/abs/10.1137/1.9780898718980},
eprint = {https://epubs.siam.org/doi/pdf/10.1137/1.9780898718980}
}

@article{dytso2018analytical,
  title={Analytical properties of generalized Gaussian distributions},
  author={Dytso, Alex and Bustin, Ronit and Poor, H Vincent and Shamai, Shlomo},
  journal={Journal of Statistical Distributions and Applications},
  volume={5},
  number={1},
  pages={6},
  year={2018},
  publisher={SpringerOpen}
}


@article{west1987scale,
  title={On scale mixtures of normal distributions},
  author={West, Mike},
  journal={Biometrika},
  volume={74},
  number={3},
  pages={646--648},
  year={1987},
  publisher={Oxford University Press}
}

@article{Bhadra2017subset,
author = {Bhadra, Anindya and Datta, Jyotishka and Polson, Nicholas G and Willard, Brandon},
title = {{Horseshoe Regularization for Feature Subset Selection}},
journal = {Sankhya B, special issue in memory of Jayanta K. Ghosh},
year = {2019},
volume={to appear},
pages = {arXiv:1702.07400},
month = feb
}


@misc{Matern1960,
title = {\emph{Spatial variation}, {Meddelanden fran Statens Skogsforskningsinstitut, 49, 5. Second ed. (1986), Lecture Notes in Statistics 36, New York: Springer} },
author = {Mat{\'e}rn, Bertil},
year = {1960}
}

@book{Stein1999,
author = {Stein, Michael L},
title = {Interpolation of Spatial Data: Some Theory for Kriging},
publisher = {Springer Science {\&} Business Media},
year = {1999},
month = jul
}

@article{gordy1998computationally,
  title={Computationally convenient distributional assumptions for common-value auctions},
  author={Gordy, Michael B},
  journal={Computational Economics},
  volume={12},
  number={1},
  pages={61--78},
  year={1998},
  publisher={Springer}
}
@article{cutillo2008larger,
  title={Larger posterior mode wavelet thresholding and applications},
  author={Cutillo, Luisa and Jung, Yoon Young and Ruggeri, Fabrizio and Vidakovic, Brani},
  journal={Journal of Statistical Planning and Inference},
  volume={138},
  number={12},
  pages={3758--3773},
  year={2008},
  publisher={Elsevier}
}
@article{belitser2015needles,
  title={Needles and straw in a haystack: robust confidence for possibly sparse sequences},
  author={Belitser, Eduard and Nurushev, Nurzhan},
  journal={arXiv preprint arXiv:1511.01803},
  year={2015}
}

@article{baranchik1970family,
  title={A family of minimax estimators of the mean of a multivariate normal distribution},
  author={Baranchik, Alvin J},
  journal={The Annals of Mathematical Statistics},
  volume={41},
  pages={642--645},
  year={1970},
  publisher={JSTOR}
}

@article{kingma2013auto,
  title={Auto-encoding variational {B}ayes},
  author={Kingma, Diederik P and Welling, Max},
  journal={arXiv preprint arXiv:1312.6114},
  year={2013}
}

@article{carpenter2017stan,
  title={Stan: A probabilistic programming language},
  author={Carpenter, Bob and Gelman, Andrew and Hoffman, Matthew D and Lee, Daniel and Goodrich, Ben and Betancourt, Michael and Brubaker, Marcus and Guo, Jiqiang and Li, Peter and Riddell, Allen},
  journal={Journal of Statistical Software},
  volume={76},
  number={1},
  pages={1--32},
  year={2017},
  publisher={Columbia Univ., New York, NY (United States); Harvard Univ., Cambridge, MA~Ã}
}

@incollection{zellner86,
author={Zellner, A.},
title={On assessing prior distributions and {B}ayesian regression analysis with g-prior
distributions},
booktitle={ Bayesian Inference and Decision Techniques: Essays in Honor of Bruno de Finetti},
editor={Goel, P. K. and Zellner, A.},
pages={233--243},
year={1986},
publisher={Elsevier, North-Holland}
}


@article{nishimura2018prior,
  title={Prior-preconditioned conjugate gradient for accelerated {G}ibbs sampling in ``large $n$ \& large $p$'' sparse {B}ayesian regression},
  author={Nishimura, Akihiko and Suchard, Marc A},
  journal={arXiv preprint arXiv:1810.12437},
  year={2018}
}

@article{bai2018large,
  title={Large-Scale Multiple Hypothesis Testing with the Normal-Beta Prime Prior},
  author={Bai, Ray and Ghosh, Malay},
  journal={arXiv preprint arXiv:1807.02421},
  year={2018}
}

@article{yao2019quality,
  title={Quality of Uncertainty Quantification for {B}ayesian Neural Network Inference},
  author={Yao, Jiayu and Pan, Weiwei and Ghosh, Soumya and Doshi-Velez, Finale},
  journal={arXiv preprint arXiv:1906.09686},
  year={2019}
}



@article{ghosh2016asymptotic,
  title={Asymptotic properties of {B}ayes risk of a general class of shrinkage priors in multiple hypothesis testing under sparsity},
  author={Ghosh, Prasenjit and Tang, Xueying and Ghosh, Malay and Chakrabarti, Arijit},
  journal={Bayesian Analysis},
  volume={11},
  number={3},
  pages={753--796},
  year={2016},
  publisher={International Society for Bayesian Analysis}
}

@article{ghosh2017asymptotic,
  title={Asymptotic optimality of one-group shrinkage priors in sparse high-dimensional problems},
  author={Ghosh, Prasenjit and Chakrabarti, Arijit},
  journal={Bayesian Analysis},
  volume={12},
  number={4},
  pages={1133--1161},
  year={2017},
  publisher={International Society for Bayesian Analysis}
}


@inproceedings{abadi2016tensorflow,
  title={Tensorflow: A system for large-scale machine learning},
  author={Abadi, Mart{\'\i}n and Barham, Paul and Chen, Jianmin and Chen, Zhifeng and Davis, Andy and Dean, Jeffrey and Devin, Matthieu and Ghemawat, Sanjay and Irving, Geoffrey and Isard, Michael and others},
  booktitle={12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16)},
  pages={265--283},
  year={2016}
}


@article{jacquier2007mcmc,
  title={{MCMC} maximum likelihood for latent state models},
  author={Jacquier, Eric and Johannes, Michael and Polson, Nicholas},
  journal={Journal of Econometrics},
  volume={137},
  number={2},
  pages={615--640},
  year={2007},
  publisher={Elsevier}
}


@article{neville2014mean,
  title={Mean field variational {B}ayes for continuous sparse signal shrinkage: pitfalls and remedies},
  author={Neville, Sarah E and Ormerod, John T and Wand, M P},
  journal={Electronic Journal of Statistics},
  volume={8},
  number={1},
  pages={1113--1151},
  year={2014},
  publisher={The Institute of Mathematical Statistics and the Bernoulli Society}
}


@article{vecchia1988estimation,
  title={Estimation and model identification for continuous spatial processes},
  author={Vecchia, Aldo V},
  journal={Journal of the Royal Statistical Society: Series B (Methodological)},
  volume={50},
  number={2},
  pages={297--312},
  year={1988},
  publisher={Wiley Online Library}
}



@article{wang2004lack,
  title={Lack of consistency of mean field and variational {B}ayes approximations for state space models},
  author={Wang, Bo and Titterington, DM},
  journal={Neural Processing Letters},
  volume={20},
  number={3},
  pages={151--170},
  year={2004},
  publisher={Springer}
}


@article{shin2018neuronized,
  title={Neuronized Priors for {B}ayesian Sparse Linear Regression},
  author={Shin, Minsuk and Liu, Jun S},
  journal={arXiv preprint arXiv:1810.00141},
  year={2018}
}

@article{li2019joint,
  title={Joint Mean-Covariance Estimation via the Horseshoe with an Application in Genomic Data Analysis},
  author={Li, Yunfan and Datta, Jyotishka and Craig, Bruce A and Bhadra, Anindya},
  journal={arXiv preprint arXiv:1903.06768},
  year={2019},
  sortname={Li,Yunfan and others},
  sortyear={2019-2},
}

@inproceedings{nalisnickautomatic,
  title={Automatic Depth Determination for {B}ayesian {R}es{N}ets},
  author={Nalisnick, Eric and Hern{\'a}ndez-Lobato, Jos{\'e} Miguel},
  booktitle={Third workshop on Bayesian Deep Learning (NeurIPS 2018)},
  year={2018}
}


@article{scott2010parameter,
  title={Parameter expansion in local-shrinkage models},
  author={Scott, James G},
  journal={arXiv preprint arXiv:1010.5265},
  year={2010}
}


@article{hahn2018efficient,
  title={Efficient sampling for {G}aussian linear regression with arbitrary priors},
  author={Hahn, P Richard and He, Jingyu and Lopes, Hedibert F},
  journal={Journal of Computational and Graphical Statistics},
  pages={1--13},
  year={2018},
  publisher={Taylor \& Francis}
}


@article{williams2018bayesian,
  title={Bayesian Estimation of {G}aussian Graphical Models with Predictive Covariance Selection},
  author={Williams, Donald R and Piironen, Juho and Vehtari, Aki and Rast, Philippe},
  journal={arXiv preprint arXiv:1801.05725},
  year={2018}
}


@article{johndrow2017scalable,
  title={Scalable {MCMC} for {B}ayes shrinkage priors},
  author={Johndrow, James E and Orenstein, Paulo and Bhattacharya, Anirban},
  journal={arXiv preprint arXiv:1705.00841},
  year={2017}
}


@article{dawid1981some,
  title={Some matrix-variate distribution theory: notational considerations and a Bayesian application},
  author={Dawid, A Philip},
  journal={Biometrika},
  volume={68},
  number={1},
  pages={265--274},
  year={1981},
  publisher={Oxford University Press}
}

@article{meinshausen2006high,
  title={High-dimensional graphs and variable selection with the lasso},
  author={Meinshausen, Nicolai and B{\"u}hlmann, Peter},
  journal={The Annals of Statistics},
  volume={34},
  number={3},
  pages={1436--1462},
  year={2006},
  publisher={Institute of Mathematical Statistics}
}


@article{tran2018bayesian,
  title={Bayesian Deep Net GLM and GLMM},
  author={Tran, Minh-Ngoc and Nguyen, Nghia and Nott, David and Kohn, Robert},
  journal={arXiv preprint arXiv:1805.10157},
  year={2018}
}

@article{hahn18,
author = {P. Richard Hahn and Jingyu He and Hedibert Lopes},
title = {Bayesian Factor Model Shrinkage for Linear {IV} Regression With Many Instruments},
journal = {Journal of Business \& Economic Statistics},
volume = {36},
number = {2},
pages = {278-287},
year  = {2018},
publisher = {Taylor & Francis},
doi = {10.1080/07350015.2016.1172968},

URL = { 
        https://doi.org/10.1080/07350015.2016.1172968
    
},
eprint = { 
        https://doi.org/10.1080/07350015.2016.1172968
    
}

}


@article{zellner1962efficient,
  title={An efficient method of estimating seemingly unrelated regressions and tests for aggregation bias},
  author={Zellner, Arnold},
  journal={Journal of the American statistical Association},
  volume={57},
  number={298},
  pages={348--368},
  year={1962},
  publisher={Taylor \& Francis}
}


@inproceedings{gan2015learning,
  title={Learning deep sigmoid belief networks with data augmentation},
  author={Gan, Zhe and Henao, Ricardo and Carlson, David and Carin, Lawrence},
  booktitle={Artificial Intelligence and Statistics},
  pages={268--276},
  year={2015}
}

@article{nalisnick2018unifying,
  author = {{Nalisnick}, Eric and {Hern{\'a}ndez-Lobato}, Jos{\'e} Miguel and
         {Smyth}, Padhraic},
        title = "{Dropout as a Structured Shrinkage Prior}",
      journal = {arXiv e-prints},
     keywords = {Statistics - Machine Learning, Computer Science - Machine Learning},
         year = "2018",
        month = "Oct",
          eid = {arXiv:1810.04045},
        pages = {arXiv:1810.04045},
archivePrefix = {arXiv},
       eprint = {1810.04045},
 primaryClass = {stat.ML},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2018arXiv181004045N},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}


@article{terenin2018gpu,
  title={{GPU}-accelerated {G}ibbs sampling: a case study of the Horseshoe Probit model},
  author={Terenin, Alexander and Dong, Shawfeng and Draper, David},
  journal={Statistics and Computing},
  pages={301--310},
  volume={29},
  year={2019},
  publisher={Springer}
}


@article {Pritchard945,
  author = {Pritchard, Jonathan K. and Stephens, Matthew and Donnelly, Peter},
  title = {Inference of Population Structure Using Multilocus Genotype Data},
  volume = {155},
  number = {2},
  pages = {945--959},
  year = {2000},
  publisher = {Genetics},
  issn = {0016-6731},
  URL = {http://www.genetics.org/content/155/2/945},
  eprint = {http://www.genetics.org/content/155/2/945.full.pdf},
  journal = {Genetics}
}

@inproceedings{lee2018deep,
title={Deep Neural Networks as {Gaussian} Processes},
author={Jaehoon Lee and Jascha Sohl-dickstein and Jeffrey Pennington and Roman Novak and Sam Schoenholz and Yasaman Bahri},
booktitle={International Conference on Learning Representations},
year={2018},
url={https://openreview.net/forum?id=B1EA-M-0Z},
}


@article{srivastava2014dropout,
  title={Dropout: a simple way to prevent neural networks from overfitting},
  author={Srivastava, Nitish and Hinton, Geoffrey and Krizhevsky, Alex and Sutskever, Ilya and Salakhutdinov, Ruslan},
  journal={Journal of Machine Learning Research},
  volume={15},
  number={1},
  pages={1929--1958},
  year={2014},
  publisher={JMLR. org}
}


@inproceedings{garriga-alonso2018deep,
title={Deep Convolutional Networks as shallow {Gaussian Processes}},
author={Adri Garriga-Alonso and Carl Edward Rasmussen and Laurence Aitchison},
booktitle={International Conference on Learning Representations},
year={2019},
url={https://openreview.net/forum?id=Bklfsi0cKm},
}


@article{blei2003latent,
  title={Latent {D}irichlet allocation},
  author={Blei, David M and Ng, Andrew Y and Jordan, Michael I},
  journal={Journal of machine Learning research},
  volume={3},
  number={Jan},
  pages={993--1022},
  year={2003}
}

@article{wei2017contraction,
  title={Contraction properties of shrinkage priors in logistic regression},
  author={Wei, Ran and Ghosal, Subhashis},
  journal={Technical report, Department of Statistics, North Carolina State University},
  year={2017},
  url={https://www4.stat.ncsu.edu/~sghosal/papers/Shrinkage%20Logistic.pdf}
}

@inproceedings{ghosh17,
  title = {Model Selection in {B}ayesian Neural Networks via Horseshoe Priors},
  booktitle = {Neural Information Processing Systems (NIPS) Workshop on Bayesian Deep Learning},
  year = {2017},
  author = {Soumya Ghosh and Finale Doshi-Velez}
}

@inproceedings{ghosh2018structured,
itle =   {Structured Variational Learning of {B}ayesian Neural Networks with Horseshoe Priors},
  author =   {Ghosh, Soumya and Yao, Jiayu and Doshi-Velez, Finale},
  booktitle =    {Proceedings of the 35th International Conference on Machine Learning},
  pages =    {1744--1753},
  year =   {2018},
  editor =   {Dy, Jennifer and Krause, Andreas},
  volume =   {80},
  series =   {Proceedings of Machine Learning Research},
  address =    {Stockholm, Sweden},
  month =    {10--15 Jul},
  publisher =    {PMLR},
  pdf =    {http://proceedings.mlr.press/v80/ghosh18a/ghosh18a.pdf},
  url =    {http://proceedings.mlr.press/v80/ghosh18a.html},
  abstract =   {Bayesian Neural Networks (BNNs) have recently received increasing attention for their ability to provide well-calibrated posterior uncertainties. However, model selectionÃ¢ÂÂeven choosing the number of nodesÃ¢ÂÂremains an open question. Recent work has proposed the use of a horseshoe prior over node pre-activations of a Bayesian neural network, which effectively turns off nodes that do not help explain the data. In this work, we propose several modeling and inference advances that consistently improve the compactness of the model learned while maintaining predictive performance, especially in smaller-sample settings including reinforcement learning.}

}


@inproceedings{johndrow2013diagonal,
  title={Diagonal orthant multinomial probit models},
  author={Johndrow, James and Dunson, David and Lum, Kristian},
  booktitle={Artificial Intelligence and Statistics},
  pages={29--38},
  year={2013}
}

@article{atchade2017contraction,
  title={On the contraction properties of some high-dimensional quasi-posterior distributions},
  author={Atchad{\'e}, Yves A},
  journal={The Annals of Statistics},
  volume={45},
  number={5},
  pages={2248--2273},
  year={2017},
  publisher={Institute of Mathematical Statistics}
}



@article{magnusson2016dolda,
  title={{DOLDA}-a regularized supervised topic model for high-dimensional multi-class regression},
  author={Magnusson, M{\aa}ns and Jonsson, Leif and Villani, Mattias},
  journal={arXiv preprint arXiv:1602.00260},
  year={2016}
}



@InProceedings{piironen2016hyperprior,
  title =    {{On the Hyperprior Choice for the Global Shrinkage Parameter in the Horseshoe Prior}},
  author =   {Juho Piironen and Aki Vehtari},
  booktitle =    {Proceedings of the 20th International Conference on Artificial Intelligence and Statistics},
  pages =    {905--913},
  year =   {2017},
  editor =   {Aarti Singh and Jerry Zhu},
  volume =   {54},
  series =   {Proceedings of Machine Learning Research},
  address =    {Fort Lauderdale, FL, USA},
  month =    {20--22 Apr},
  publisher =    {PMLR},
  pdf =    {http://proceedings.mlr.press/v54/piironen17a/piironen17a.pdf},
  url =    {http://proceedings.mlr.press/v54/piironen17a.html},
  abstract =   {The horseshoe prior has proven to be a noteworthy alternative for sparse Bayesian estimation, but as shown in this paper, the results can be sensitive to the prior choice for the global shrinkage hyperparameter. We argue that the previous default choices are dubious due to their tendency to favor solutions with more unshrunk coefficients than we typically expect a priori. This can lead to bad results if this parameter is not strongly identified by data. We derive the relationship between the global parameter and the effective number of nonzeros in the coefficient vector, and show an easy and intuitive way of setting up the prior for the global parameter based on our prior beliefs about the number of nonzero coefficients in the model. The results on real world data show that one can benefit greatly Ã¢ÂÂ in terms of improved parameter estimates, prediction accuracy, and reduced computation time Ã¢ÂÂ from transforming even a crude guess for the number of nonzero coefficients into the prior for the global parameter using our framework.}
}



@article{li2018bayesian,
  title={Bayesian Joint Spike-and-Slab Graphical Lasso},
  author={Li, Zehang Richard and McCormick, Tyler H and Clark, Samuel J},
  journal={arXiv preprint arXiv:1805.07051},
  year={2018}
}


@article{deshpande2017simultaneous,
  title={Simultaneous Variable and Covariance Selection with the Multivariate Spike-and-Slab Lasso},
  author={Deshpande, Sameer K and Ro{\v{c}}kov{\'a}, Veronika and George, Edward I},
  journal={Journal of Computational and Graphical Statistics},
  volume={to appear},
  year={2019}
}


@inproceedings{louizos2017bayesian,
  title={Bayesian compression for deep learning},
  author={Louizos, Christos and Ullrich, Karen and Welling, Max},
  booktitle={Advances in Neural Information Processing Systems},
  pages={3288--3298},
  year={2017}
}

@article{bhadra2013joint,
  title={Joint High-Dimensional {B}ayesian Variable and Covariance Selection with an Application to e{QTL} Analysis},
  author={Bhadra, Anindya and Mallick, Bani K},
  journal={Biometrics},
  volume={69},
  number={2},
  pages={447--457},
  year={2013}
}

@article{bhadra2011malaria,
  title={Malaria in Northwest India: Data analysis via partially observed stochastic differential equation models driven by L{\'e}vy noise},
  author={Bhadra, Anindya and Ionides, Edward L and Laneri, Karina and Pascual, Mercedes and Bouma, Menno and Dhiman, Ramesh C},
  journal={Journal of the American Statistical Association},
  volume={106},
  number={494},
  pages={440--451},
  year={2011},
  publisher={ASA}
}

@article{ionides2011iterated,
  title={Iterated filtering},
  author={Ionides, Edward L and Bhadra, Anindya and Atchad{\'e}, Yves and King, Aaron},
  journal={The Annals of Statistics},
  volume={39},
  number={3},
  pages={1776--1802},
  year={2011},
  publisher={Institute of Mathematical Statistics}
}


@article{bitto2018achieving,
  title={Achieving shrinkage in a time-varying parameter model framework},
  author={Bitto, Angela and Fr{\"u}hwirth-Schnatter, Sylvia},
  journal={Journal of Econometrics},
  year={2018},
  publisher={Elsevier}
}

@article{castillo2017discussion,
  title={Discussion on article by van der Pas, Szab\'o, and van der Vaart.},
  author={Castillo, Isma\"el},
  journal={Bayesian Analysis},
  volume={12},
  year={2017}
}
@article{li2017graphical,
  title={The Graphical Horseshoe Estimator for Inverse Covariance Matrices},
  author={Li, Yunfan and Craig, Bruce A and Bhadra, Anindya},
  journal={Journal of Computational and Graphical Statistics},
  volume={28},
  pages={747--757},
  year={2019},
  sortname={Li,Yunfan and others},
  sortyear={2019-1}
}

@article{dawid1973posterior,
  title={Posterior expectations for large observations},
  author={Dawid, A Philip},
  journal={Biometrika},
  volume={60},
  number={3},
  pages={664--667},
  year={1973},
  publisher={Biometrika Trust}
}



@inproceedings{louizos2017bayesian,
  title={Bayesian compression for deep learning},
  author={Louizos, Christos and Ullrich, Karen and Welling, Max},
  booktitle={Advances in Neural Information Processing Systems},
  pages={3290--3300},
  year={2017}
}

@article{wang2012bayesiangl,
  title={Bayesian graphical lasso models and efficient posterior computation},
  author={Wang, Hao},
  journal={Bayesian Analysis},
  volume={7},
  number={4},
  pages={867--886},
  year={2012},
  publisher={International Society for Bayesian Analysis}
}

@article{shin2016functional,
  title={Functional Horseshoe Priors for Subspace Shrinkage},
  author={Shin, Minsuk and Bhattacharya, Anirban and Johnson, Valen E},
  journal={arXiv preprint arXiv:1606.05021},
  year={2016}
}

@book{fourdrinier2017shrinkage,
  title={Shrinkage Estimation},
  author={Fourdrinier, Dominique and Strawderman, William E and Wells, Martin T},
  year={2018},
  publisher={Springer, New York}
}

@article{makalic2016samplerHS,
  title={A simple sampler for the horseshoe estimator},
  author={Makalic, Enes and Schmidt, Daniel F},
  journal={IEEE Signal Processing Letters},
  volume={23},
  number={1},
  pages={179--182},
  year={2016},
  publisher={IEEE}
}

@article{friedman2008glasso,
  title={Sparse inverse covariance estimation with the graphical lasso},
  author={Friedman, Jerome and Hastie, Trevor and Tibshirani, Robert},
  journal={Biostatistics},
  volume={9},
  number={3},
  pages={432--441},
  year={2008},
  publisher={Biometrika Trust}
}


@article{Datta2015,
author = {Datta, Abhirup and Banerjee, Sudipto and Finley, Andrew O and Gelfand, Alan E},
title = {{Hierarchical nearest-neighbor Gaussian process models for large geostatistical datasets}},
journal = {Journal of the American Statistical Association},
year = {2016},
volume = {111},
number = {514},
pages = {800--812},
month = aug
}


@article{bhadra2017lasso,
  title={Lasso Meets Horseshoe: a survey},
  author={Bhadra, Anindya and Datta, Jyotishka and Polson, Nicholas G and Willard, Brandon T},
  journal={Statistical Science},
  volume={34},
  pages={405--427},
  year={2019}
}

@article{nalenz2017tree,
  title={Tree Ensembles with Rule Structured Horseshoe Regularization},
  author={Nalenz, Malte and Villani, Mattias},
  journal={arXiv preprint arXiv:1702.05008},
  year={2017}
}
@phdthesis{wei2017bayesian,
  title={Bayesian Variable Selection Using Continuous Shrinkage Priors for Nonparametric Models and Non-Gaussian Data.},
  author={Wei, Ran},
 school = {North Carolina State University},
  year={2017}
}
@article{kowal2017dynamic,
  title={Dynamic Shrinkage Processes},
  author={Kowal, Daniel R and Matteson, David S and Ruppert, David},
  journal={Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
  volume={to appear},
  year={2019}
}
@article{lam2009sparsistency,
  title={Sparsistency and rates of convergence in large covariance matrix estimation},
  author={Lam, Clifford and Fan, Jianqing},
  journal={Annals of Statistics},
  volume={37},
  number={6B},
  pages={4254--4278},
  year={2009},
  publisher={NIH Public Access}
}

@book{lauritzen1996graphical,
  title={Graphical models},
  author={Lauritzen, Steffen L},
  volume={17},
  year={1996},
  publisher={Clarendon Press}
}


@article{armagan2013posterior,
  title={Posterior consistency in linear models under shrinkage priors},
  author={Armagan, Artin and Dunson, David B and Lee, Jaeyong and Bajwa, Waheed U and Strawn, Nate},
  journal={Biometrika},
  volume={100},
  number={4},
  pages={1011--1018},
  year={2013},
  publisher={Oxford University Press}
}
@article{li1989honest,
  title={Honest confidence regions for nonparametric regression},
  author={Li, Ker-Chau},
  journal={The Annals of Statistics},
  pages={1001--1008},
  year={1989},
  publisher={JSTOR}
}
@article{javanmard2014confidence,
  title={Confidence intervals and hypothesis testing for high-dimensional regression},
  author={Javanmard, Adel and Montanari, Andrea},
  journal={The Journal of Machine Learning Research},
  volume={15},
  number={1},
  pages={2869--2909},
  year={2014},
  publisher={JMLR. org}
}
@article{zhang2014confidence,
  title={Confidence intervals for low dimensional parameters in high dimensional linear models},
  author={Zhang, Cun-Hui and Zhang, Stephanie S},
  journal={Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
  volume={76},
  number={1},
  pages={217--242},
  year={2014},
  publisher={Wiley Online Library}
}
@article{van2014asymptotically,
  title={On asymptotically optimal confidence regions and tests for high-dimensional models},
  author={Van de Geer, Sara and B{\"u}hlmann, Peter and Ritov, YaÃ¢ÂÂacov and Dezeure, Ruben and others},
  journal={The Annals of Statistics},
  volume={42},
  number={3},
  pages={1166--1202},
  year={2014},
  publisher={Institute of Mathematical Statistics}
}
@article{nickl2013confidence,
  title={Confidence sets in sparse regression},
  author={Nickl, Richard and van de Geer, Sara and others},
  journal={The Annals of Statistics},
  volume={41},
  number={6},
  pages={2852--2876},
  year={2013},
  publisher={Institute of Mathematical Statistics}
}
@article{liu2013asymptotic,
  title={Asymptotic properties of Lasso+ mLS and Lasso+ Ridge in sparse high-dimensional linear regression},
  author={Liu, Hanzhong and Yu, Bin},
  journal={Electronic Journal of Statistics},
  volume={7},
  pages={3124--3169},
  year={2013},
  publisher={The Institute of Mathematical Statistics and the Bernoulli Society}
}
@article{chatterjee2011bootstrapping,
  title={Bootstrapping lasso estimators},
  author={Chatterjee, Arindam and Lahiri, Soumendra Nath},
  journal={Journal of the American Statistical Association},
  volume={106},
  number={494},
  pages={608--625},
  year={2011},
  publisher={Taylor \& Francis}
}
@article{tang2016bayesian,
  title={Bayesian Variable Selection and Estimation Based on Global-Local Shrinkage Priors},
  author={Tang, Xueying and Xu, Xiaofan and Ghosh, Malay and Ghosh, Prasenjit},
  journal={Sankhya A},
  pages={1--32},
  year={2016},
  publisher={Springer}
}
@article{donoho1995adapting,
  title={Adapting to unknown smoothness via wavelet shrinkage},
  author={Donoho, David L and Johnstone, Iain M},
  journal={Journal of the american statistical association},
  volume={90},
  number={432},
  pages={1200--1224},
  year={1995},
  publisher={Taylor \& Francis Group}
}
@article{polson2012local,
  title={Local shrinkage rules, L{\'e}vy processes and regularized regression},
  author={Polson, Nicholas G and Scott, James G},
  journal={Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
  volume={74},
  number={2},
  pages={287--311},
  year={2012},
  publisher={Wiley Online Library}
}


@article{bochner1933monotone,
  title={Monotone funktionen, stieltjessche integrale und harmonische analyse},
  author={Bochner, Salomon},
  journal={Mathematische Annalen},
  volume={108},
  number={1},
  pages={378--410},
  year={1933},
  publisher={Springer}
}

@article{datta2015search,
  title={In Search of Optimal Objective Priors for Model Selection and Estimation},
  author={Datta, Jyotishka and Ghosh, Jayanta K},
  journal={Current Trends in Bayesian Methodology with Applications},
  pages={225},
  year={2015},
  publisher={CRC Press}
}
@article{hahn2017efficient,
  title={Efficient sampling for {G}aussian linear regression with arbitrary priors},
  author={Hahn, P Richard and He, Jingyu and Lopes, Hedibert F},
  year={2017}
}
@book{jeffreys1972methods,
  title={{Methods of Mathematical Physics}},
  author={Jeffreys, Harold and Swirles, Bertha},
  year={1972},
  edition = {3},
  publisher={Cambridge university press},
  address   = {Cambridge}
}
@article{gelman2014understanding,
  title={Understanding predictive information criteria for Bayesian models},
  author={Gelman, Andrew and Hwang, Jessica and Vehtari, Aki},
  journal={Statistics and computing},
  volume={24},
  number={6},
  pages={997--1016},
  year={2014},
  publisher={Springer}
}
@article{piironen2017sparsity,
  title={Sparsity information and regularization in the horseshoe and other shrinkage priors},
  author={Piironen, Juho and Vehtari, Aki},
  journal={Electronic Journal of Statistics},
  volume={11},
  number={2},
  pages={5018--5051},
  year={2017},
  publisher={The Institute of Mathematical Statistics and the Bernoulli Society}
}

@article{zhao2006model,
  title={On model selection consistency of Lasso},
  author={Zhao, Peng and Yu, Bin},
  journal={Journal of Machine learning research},
  volume={7},
  number={Nov},
  pages={2541--2563},
  year={2006}
}

@article{chernozhukov2017lava,
  title={A lava attack on the recovery of sums of dense and sparse signals},
  author={Chernozhukov, Victor and Hansen, Christian and Liao, Yuan and others},
  journal={The Annals of Statistics},
  volume={45},
  number={1},
  pages={39--76},
  year={2017},
  publisher={Institute of Mathematical Statistics}
}
@article{tikhonov1963solution,
  title={Solution of incorrectly formulated problems and the regularization method},
  author={Tikhonov, Andrey},
  journal={Soviet Meth. Dokl.},
  volume={4},
  pages={1035--1038},
  year={1963}
}
@article{bai2017inverse,
  title={The Inverse Gamma-Gamma Prior for Optimal Posterior Contraction and Multiple Hypothesis Testing},
  author={Bai, Ray and Ghosh, Malay},
  journal={arXiv preprint arXiv:1710.04369},
  year={2017}
}
@article{berger_development_1992,
  title = {On the Development of Reference Priors},
  volume = {4},
  timestamp = {2015-06-13T01:29:58Z},
  number = {4},
  journal = {Bayesian statistics},
  author = {Berger, James O and Bernardo, Jos{\'e} M},
  year = {1992},
  pages = {35--60}
}

@book{hastie_elements_2009,
  title = {The Elements of Statistical Learning},
  volume = {2},
  timestamp = {2017-05-24T02:08:47Z},
  number = {1},
  publisher = {{Springer}},
  author = {Hastie, Trevor and Tibshirani, Robert and Friedman, Jerome and Hastie, T and Friedman, J and Tibshirani, R},
  year = {2009}
}

@article{bernardo1979reference,
  title={{Reference posterior distributions for {B}ayesian inference (with discussion)}},
  author={Bernardo, Jose M},
  journal={Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
  volume={41},
  pages={113--147},
  year={1979},
  publisher={JSTOR}
}
@article{barndorff1982normal,
  title={Normal variance-mean mixtures and $z$ distributions},
  author={Barndorff-Nielsen, O. E. and Kent, John T. and S{\o}rensen, Michael},
  journal={International Statistical Review},
volume={50},
  pages={145--159},
  year={1982},
  publisher={JSTOR}
}


@article{barn77,
    author = {{Barndorff-Nielsen}, O. E.},
    citeulike-article-id = {10910219},
    citeulike-linkout-0 = {http://dx.doi.org/10.1098/rspa.1977.0041},
    citeulike-linkout-1 = {http://adsabs.harvard.edu/cgi-bin/nph-bib\_query?bibcode=1977RSPSA.353..401B},
    doi = {10.1098/rspa.1977.0041},
    journal = {Royal Society of London Proceedings Series A},
    keywords = {power\_law},
    month = mar,
    pages = {401--419},
    posted-at = {2012-07-21 20:24:03},
    priority = {2},
    title = {{Exponentially Decreasing Distributions for the Logarithm of Particle Size}},
    url = {http://dx.doi.org/10.1098/rspa.1977.0041},
    volume = {353},
    year = {1977}
}

@book{bingham1989regular,
    AUTHOR = {Bingham, N. H. and Goldie, C. M. and Teugels, J. L.},
     TITLE = {Regular {V}ariation},
    SERIES = {Encyclopedia of Mathematics and its Applications},
    VOLUME = {27},
 PUBLISHER = {Cambridge University Press, Cambridge},
      YEAR = {1989},
     PAGES = {xx+494},
      ISBN = {0-521-37943-1},
   MRCLASS = {26A12 (11-01)},
  MRNUMBER = {1015093},
}



@article{polson2013bayesian,
  title={Bayesian inference for logistic models using {P}{\'o}lya--Gamma latent variables},
  author={Polson, Nicholas G and Scott, James G and Windle, Jesse},
  journal={Journal of the American statistical Association},
  volume={108},
  number={504},
  pages={1339--1349},
  year={2013},
  publisher={Taylor \& Francis}
}

@article{barnhal77,
year={1977},
issn={0044-3719},
journal={Zeitschrift f{\"u}r Wahrscheinlichkeitstheorie und Verwandte Gebiete},
volume={38},
number={4},
doi={10.1007/BF00533162},
title={Infinite divisibility of the hyperbolic and generalized inverse Gaussian distributions},
url={http://dx.doi.org/10.1007/BF00533162},
publisher={Springer-Verlag},
author={Barndorff-Nielsen, O. E. and Halgreen, Christian},
pages={309-311},
language={English}
}

@article{barndorff78,
  title={Hyperbolic distributions and distributions on hyperbolae},
   author = {{Barndorff-Nielsen}, O. E.},
  journal={Scandinavian Journal of Statistics},
volume={5},
  pages="151-157",
  year={1978},
  publisher={JSTOR}
}

@article{kowal2017dynamic,
  title={Dynamic Shrinkage Processes},
  author={Kowal, Daniel R and Matteson, David S and Ruppert, David},
  journal={arXiv preprint arXiv:1707.00763},
  year={2017}
}

@article{datta16,
author = {Datta, Jyotishka and Dunson, David B.},
title = {Bayesian inference on quasi-sparse count data},
journal = {Biometrika},
volume = {103},
number = {4},
pages = {971-983},
year = {2016},
doi = {10.1093/biomet/asw053},
URL = {http://dx.doi.org/10.1093/biomet/asw053},
eprint = {/oup/backfile/content_public/journal/biomet/103/4/10.1093_biomet_asw053/3/asw053.pdf}
}


@article{albert1993bayesian,
  title={Bayesian analysis of binary and polychotomous response data},
  author={Albert, James H and Chib, Siddhartha},
  journal={Journal of the American statistical Association},
  volume={88},
  number={422},
  pages={669--679},
  year={1993},
  publisher={Taylor \& Francis Group}
}


@inproceedings{james_estimation_1961,
  title = {Estimation with Quadratic Loss},
  volume = {1},
  timestamp = {2015-07-24T16:06:42Z},
  urldate = {2015-07-24},
  url = {https://books.google.com/books?hl=en\&lr=\&id=RfdwmhDpcl8C\&oi=fnd\&pg=PA361\&dq=stein+estimation+quadratic+loss\&ots=quDgDFm4FM\&sig=ssVvRnRggRv7bAL7p-xGOqBacGc},
  booktitle = {Proceedings of the Fourth {{Berkeley}} Symposium on Mathematical Statistics and Probability},
  author = {James, William and Stein, Charles},
  year = {1961},
  pages = {361--379}
}

@article{bien_lasso_2013,
  title = {A Lasso for Hierarchical Interactions},
  volume = {41},
  issn = {00905364},
  doi = {10.1214/13-AOS1096},
  timestamp = {2017-05-24T02:09:01Z},
  number = {3},
  journal = {Annals of Statistics},
  author = {Bien, Jacob and Taylor, Jonathan and Tibshirani, Robert},
  year = {2013},
  keywords = {convexity,Hierarchical sparsity,Interactions,lasso,Regularized regression},
  pages = {1111--1141}
}

@article{polson2013data,
  title={Data augmentation for non-{G}aussian regression models using variance-mean mixtures},
  author={Polson, Nicholas G and Scott, James G},
  journal={Biometrika},
  volume={100},
  number={2},
  pages={459--471},
  year={2013},
  publisher={Oxford University Press}
}



@article{wang2019scalable,
  title={Scalable Data Augmentation for Deep Learning},
  author={Wang, Yuexi and Polson, Nicholas G and Sokolov, Vadim O},
  journal={arXiv preprint arXiv:1903.09668},
  year={2019}
}


@article{tibshirani_sparsity_2005,
  title = {Sparsity and Smoothness via the Fused Lasso},
  volume = {67},
  issn = {13697412},
  doi = {10.1111/j.1467-9868.2005.00490.x},
  timestamp = {2017-05-24T02:08:30Z},
  number = {1},
  journal = {Journal of the Royal Statistical Society. Series B: Statistical Methodology},
  author = {Tibshirani, Robert and Saunders, Michael A. and Rosset, Saharon and Zhu, Ji and Knight, Keith},
  year = {2005},
  keywords = {fused lasso,Gene expression,lasso,Least squares regression,Protein mass spectroscopy,Sparse solutions,Support vector classifier},
  pages = {91--108}
}

@book{efron2016computer,
  title={Computer Age Statistical Inference},
  author={Efron, Bradley and Hastie, Trevor},
  year={2016},
  publisher={Cambridge University Press},
  address={Cambridge}
}

@article{efron1973discussion,
  title={{Discussion of ``Marginalization paradoxes in {B}ayesian and structural inference'' by A. P. Dawid, M. Stone, and J. V. Zidek}},
  author={Efron, BE},
  JOURNAL = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
    VOLUME = {35},
      YEAR = {1973},
     PAGES = {219},
      ISSN = {0035-9246},
   MRCLASS = {62C10},
  MRNUMBER = {0365805},
MRREVIEWER = {I. J. Good},
}


@article{efron_least_2004,
  title = {Least Angle Regression},
  volume = {32},
  timestamp = {2017-05-08T22:50:22Z},
  number = {2},
  urldate = {2017-01-12},
  url = {http://projecteuclid.org/euclid.aos/1083178935},
  journal = {The Annals of statistics},
  author = {Efron, Bradley and Hastie, Trevor and Johnstone, Iain and Tibshirani, Robert},
  year = {2004},
  pages = {407--499}
}

@inproceedings{robbins1956,
address = "Berkeley, Calif.",
author = "Robbins, Herbert",
booktitle = "Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Contributions to the Theory of Statistics",
pages = "157--163",
publisher = "University of California Press",
title = "An Empirical {B}ayes Approach to Statistics",
url = "https://projecteuclid.org/euclid.bsmsp/1200501653",
year = "1956"
}

@article{efron1973stein,
  title={Stein's estimation rule and its competitors--an empirical {B}ayes approach},
  author={Efron, Bradley and Morris, Carl},
  journal={Journal of the American Statistical Association},
  volume={68},
  number={341},
  pages={117--130},
  year={1973},
  publisher={Taylor \& Francis Group}
}

@book{berger1985statistical,
  title={Statistical decision theory and {B}ayesian analysis},
  author={Berger, J. O.},
  year={1985},
  publisher={Springer},
  address={New York}
}

@article{park_bayesian_2008,
  title = {The Bayesian Lasso},
  volume = {103},
  timestamp = {2017-01-17T02:13:16Z},
  number = {482},
  urldate = {2017-01-17},
  url = {http://amstat.tandfonline.com/doi/abs/10.1198/016214508000000337},
  journal = {Journal of the American Statistical Association},
  author = {Park, Trevor and Casella, George},
  year = {2008},
  pages = {681--686}
}

@article{friedman_pathwise_2007,
  title = {Pathwise Coordinate Optimization},
  volume = {1},
  timestamp = {2017-05-24T02:06:49Z},
  number = {2},
  urldate = {2017-03-23},
  url = {http://projecteuclid.org/euclid.aoas/1196438020},
  journal = {The Annals of Applied Statistics},
  author = {Friedman, Jerome and Hastie, Trevor and H{\"o}fling, Holger and Tibshirani, Robert and {others}},
  year = {2007},
  pages = {302--332}
}

@book{buhlmann2011statistics,
  title = {Statistics for High-Dimensional Data},
  timestamp = {2017-05-08T22:49:27Z},
  publisher = {{Springer-Verlag Berlin Heidelberg}},
  author = {B{\"u}hlmann, Peter and {van de Geer}, Sara},
  year = {2011}
}

@article{johnstone2004needles,
  title = {Needles and Straw in Haystacks: {{Empirical Bayes}} Estimates of Possibly Sparse Sequences},
  volume = {32},
  timestamp = {2017-05-08T22:49:27Z},
  journal = {Annals of Statistics},
  author = {Johnstone, Iain M and Silverman, Bernard W},
  year = {2004},
  pages = {1594--1649}
}

@book{efron2010large,
  title = {Large-Scale Inference: Empirical {{Bayes}} Methods for Estimation, Testing, and Prediction},
  volume = {1},
  timestamp = {2017-05-08T22:49:27Z},
  publisher = {{Cambridge University Press}},
  author = {Efron, Bradley},
  year = {2010}
}

@article{efron2008microarrays,
  title = {Microarrays, Empirical {{Bayes}} and the Two-Groups Model},
  volume = {23},
  timestamp = {2017-05-08T22:49:27Z},
  number = {1},
  journal = {Statistical Science},
  author = {Efron, Bradley},
  year = {2008},
  pages = {1--22}
}

@article{bogdan2011asymptotic,
  title = {Asymptotic {{Bayes}}-Optimality under Sparsity of Some Multiple Testing Procedures},
  volume = {39},
  timestamp = {2017-05-08T22:49:27Z},
  number = {3},
  journal = {The Annals of Statistics},
  author = {Bogdan, Ma{\l}gorzata and Chakrabarti, Arijit and Frommlet, Florian and Ghosh, Jayanta K},
  year = {2011},
  pages = {1551--1579}
}

@article{carvalho2009handling,
  title = {Handling Sparsity via the Horseshoe},
  volume = {5},
  timestamp = {2017-05-08T22:49:27Z},
  journal = {Journal of Machine Learning Research W\&CP},
  author = {Carvalho, C. M. and Polson, N. G. and Scott, J. G.},
  year = {2009},
  pages = {73--80}
}

@article{hornik1989multilayer,
  title={Multilayer feedforward networks are universal approximators},
  author={Hornik, Kurt and Stinchcombe, Maxwell and White, Halbert},
  journal={Neural networks},
  volume={2},
  number={5},
  pages={359--366},
  year={1989},
  publisher={Elsevier}
}

@article{carvalho2010horseshoe,
  title = {The Horseshoe Estimator for Sparse Signals},
  volume = {97},
  timestamp = {2017-05-08T22:49:27Z},
  journal = {Biometrika},
  author = {Carvalho, Carlos M and Polson, Nicholas G and Scott, James G},
  year = {2010},
  pages = {465--480}
}

@article{polson2010shrink,
  title = {Shrink Globally, Act Locally: Sparse {{Bayesian}} Regularization and Prediction},
  volume = {9},
  timestamp = {2017-05-08T22:49:27Z},
  journal = {Bayesian Statistics},
  author = {Polson, Nicholas G and Scott, James G},
  year = {2010},
  pages = {501--538}
}

@article{polson2012half,
  title = {On the Half-{{Cauchy}} Prior for a Global Scale Parameter},
  volume = {7},
  timestamp = {2017-05-08T22:49:27Z},
  number = {4},
  journal = {Bayesian Analysis},
  author = {Polson, Nicholas G and Scott, James G},
  year = {2012},
  pages = {887--902}
}

@article{polson2015mixtures,
  title = {Mixtures, Envelopes and Hierarchical Duality},
  volume = {78},
  timestamp = {2017-05-08T22:49:27Z},
  journal = {Journal of the Royal Statistical Society. Series B},
  author = {Polson, Nicholas G and Scott, James G},
  year = {2016},
  pages = {701--727}
}

@inproceedings{armagan2011generalized,
  title = {Generalized Beta Mixtures of {{Gaussians}}},
  timestamp = {2017-05-08T22:49:27Z},
  booktitle = {Advances in {{Neural Information Processing Systems}}},
  author = {Armagan, Artin and Clyde, Merlise and Dunson, David B},
  year = {2011},
  pages = {523--531}
}

@article{armagan2013generalized,
  title = {Generalized Double {{Pareto}} Shrinkage},
  volume = {23},
  timestamp = {2017-05-08T22:49:27Z},
  number = {1},
  journal = {Statistica Sinica},
  author = {Armagan, Artin and Dunson, David B and Lee, Jaeyong},
  year = {2013},
  pages = {119--143}
}

@article{griffin2005alternative,
  title = {Inference with Normal-Gamma Prior Distributions in Regression Problems},
  volume = {5},
  timestamp = {2017-05-08T22:49:27Z},
  number = {1},
  journal = {Bayesian Analysis},
  author = {Griffin, Jim E and Brown, Philip J},
  year = {2010},
  pages = {171--188}
}

@article{zhang2016high,
  title = {High {{Dimensional Linear Regression}} via the {{R2}}-{{D2 Shrinkage Prior}}},
  timestamp = {2017-05-08T22:49:27Z},
  journal = {arXiv preprint arXiv:1609.00046},
  author = {Zhang, Yan and Reich, Brian J and Bondell, Howard D},
  year = {2016}
}

@article{castillo2012needles,
  title = {Needles and Straw in a Haystack: {{Posterior}} Concentration for Possibly Sparse Sequences},
  volume = {40},
  timestamp = {2017-05-08T22:49:27Z},
  number = {4},
  journal = {The Annals of Statistics},
  author = {Castillo, Isma{\"e}l and {van der Vaart}, Aad},
  year = {2012},
  pages = {2069--2101}
}

@article{castillo2015bayesian,
  title = {Bayesian Linear Regression with Sparse Priors},
  volume = {43},
  timestamp = {2017-05-08T22:49:27Z},
  number = {5},
  url = {http://dx.doi.org/10.1214/15-AOS1334},
  journal = {The Annals of Statistics},
  author = {Castillo, Ismail and Schmidt-Hieber, Johannes and {van der Vaart}, Aad},
  month = oct,
  year = {2015},
  pages = {1986--2018}
}

@article{fan2001variable,
  title = {Variable Selection via Nonconcave Penalized Likelihood and Its Oracle Properties},
  volume = {96},
  timestamp = {2017-05-08T22:49:27Z},
  number = {456},
  journal = {Journal of the American statistical Association},
  author = {Fan, Jianqing and Li, Runze},
  year = {2001},
  pages = {1348--1360}
}

@article{zhang2010nearly,
  title = {Nearly Unbiased Variable Selection under Minimax Concave Penalty},
  volume = {38},
  timestamp = {2017-05-08T22:49:27Z},
  number = {2},
  journal = {The Annals of Statistics},
  author = {Zhang, Cun-Hui},
  year = {2010},
  pages = {894--942}
}

@article{mazumder2012,
  title = {{{SparseNet}}: {{Coordinate}} Descent with Nonconvex Penalties},
  volume = {106},
  timestamp = {2017-05-08T22:49:27Z},
  journal = {Journal of the American Statistical Association},
  author = {Mazumder, Rahul and Friedman, Jerome H and Hastie, Trevor},
  year = {2012},
  pages = {1125--1138}
}

@article{george2000variable,
  title = {The Variable Selection Problem},
  volume = {95},
  timestamp = {2017-05-08T22:49:27Z},
  number = {452},
  journal = {Journal of the American Statistical Association},
  author = {George, Edward I},
  year = {2000},
  pages = {1304--1308}
}

@article{George0000,
  title = {Calibration and Empirical {{Bayes}} Variable Selection},
  volume = {87},
  timestamp = {2017-05-08T22:49:27Z},
  number = {4},
  url = {http://biomet.oxfordjournals.org/content/87/4/731.abstract},
  journal = {Biometrika},
  author = {George, EdwardI. and Foster, Dean P.},
  year = {2000},
  pages = {731--747}
}

@article{george2006,
  title = {Improved Minimax Predictive Densities under {{Kullback}}-{{Leibler}} Loss},
  volume = {34},
  timestamp = {2017-05-08T22:49:27Z},
  number = {1},
  url = {http://dx.doi.org/10.1214/009053606000000155},
  journal = {The Annals of Statistics},
  author = {George, Edward I. and Liang, Feng and Xu, Xinyi},
  month = feb,
  year = {2006},
  pages = {78--91}
}

@article{mitchell88,
  title = {Bayesian {{Variable Selection}} in {{Linear Regression}}},
  volume = {83},
  issn = {01621459},
  timestamp = {2017-05-08T22:49:27Z},
  number = {404},
  url = {http://dx.doi.org/10.2307/2290129},
  journal = {Journal of the American Statistical Association},
  author = {Mitchell, T. J. and Beauchamp, J. J.},
  month = dec,
  year = {1988},
  keywords = {bayesian,graphical-models,lasso,variable-selection},
  pages = {1023--1032},
  citeulike-article-id = {4746918},
  citeulike-linkout-0 = {http://dx.doi.org/10.2307/2290129},
  citeulike-linkout-1 = {http://www.jstor.org/stable/2290129},
  posted-at = {2009-10-26 20:15:32},
  priority = {3}
}


@article{li2017variable,
  title={Variable selection using shrinkage priors},
  author={Li, Hanning and Pati, Debdeep},
  journal={Computational Statistics \& Data Analysis},
  volume={107},
  pages={107--119},
  year={2017},
  publisher={Elsevier}
}

@article{ishwaran2005spike,
    AUTHOR = {Ishwaran, Hemant and Rao, J. Sunil},
     TITLE = {Spike and slab variable selection: frequentist and {B}ayesian
              strategies},
   JOURNAL = {Ann. Statist.},
  FJOURNAL = {The Annals of Statistics},
    VOLUME = {33},
      YEAR = {2005},
    NUMBER = {2},
     PAGES = {730--773},
      ISSN = {0090-5364},
   MRCLASS = {62J05 (62C99 62J07)},
  MRNUMBER = {2163158},
MRREVIEWER = {B. M. Golam Kibria},
       DOI = {10.1214/009053604000001147},
       URL = {http://0-dx.doi.org.library.uark.edu/10.1214/009053604000001147},
}
    
@article{rovckova2014emvs,
  title={{EMVS}: The {EM} approach to {B}ayesian variable selection},
  author={Ro{\v{c}}kov{\'a}, Veronika and George, Edward I},
  journal={Journal of the American Statistical Association},
  volume={109},
  number={506},
  pages={828--846},
  year={2014},
  publisher={Taylor \& Francis}
}

@article{rovckova2016spike,
  title={The spike-and-slab lasso},
  author={Ro{\v{c}}kov{\'a}, Veronika and George, Edward I},
  journal={Journal of the American Statistical Association},
  volume={113},
  number={521},
  pages={431--444},
  year={2018},
  publisher={Taylor \& Francis}
}
}

@article{tibshirani96,
  title = {Regression {{Shrinkage}} and {{Selection}} via the {{Lasso}}},
  volume = {58},
  timestamp = {2017-05-24T02:08:10Z},
  journal = {Journal of the Royal Statistical Society. Series B},
  author = {Tibshirani, R.},
  year = {1996},
  keywords = {imported},
  pages = {267--288},
  biburl = {http://www.bibsonomy.org/bibtex/290e648276aa6cd3c601e7c0a54366233/dieudonnew},
  interhash = {334927808d42a9a6bf8eae717fed41b3},
  intrahash = {90e648276aa6cd3c601e7c0a54366233}
}

@book{hastie2015statistical,
  title = {Statistical Learning with Sparsity},
  timestamp = {2017-05-08T22:49:27Z},
  publisher = {{CRC press}},
  author = {Hastie, Trevor and Tibshirani, Robert and Wainwright, Martin},
  year = {2015}
}

@book{james2013introduction,
  title = {An Introduction to Statistical Learning},
  volume = {6},
  timestamp = {2017-05-08T22:49:27Z},
  publisher = {{Springer}},
  author = {James, Gareth and Witten, Daniela and Hastie, Trevor and Tibshirani, Robert},
  year = {2013}
}

@article{hoerl70,
  title = {Ridge {{Regression}}: {{Biased Estimation}} for {{Nonorthogonal Problems}}},
  volume = {12},
  timestamp = {2017-05-08T22:49:27Z},
  number = {1},
  url = {http://www.tandfonline.com/doi/abs/10.1080/00401706.1970.10488634},
  journal = {Technometrics},
  author = {Hoerl, Arthur E. and Kennard, Robert W.},
  year = {1970},
  pages = {55--67}
}

@article{stephens2009bayesian,
  title = {Bayesian Statistical Methods for Genetic Association Studies},
  volume = {10},
  timestamp = {2017-05-08T22:49:27Z},
  number = {10},
  journal = {Nature Reviews Genetics},
  author = {Stephens, Matthew and Balding, David J},
  year = {2009},
  pages = {681--690}
}

@article{natarajan95,
  title = {Sparse {{Approximate Solutions}} to {{Linear Systems}}},
  volume = {24},
  timestamp = {2017-05-08T22:49:27Z},
  number = {2},
  journal = {SIAM J. Comput.},
  author = {Natarajan, B. K.},
  year = {1995},
  keywords = {linear systems,Sparse solutions},
  pages = {227--234},
  issue_date = {April 1995},
  numpages = {8},
  acmid = {207987}
}

@book{boyd2004convex,
  address = {Cambridge},
  title = {Convex {{Optimization}}},
  timestamp = {2017-05-08T22:49:27Z},
  publisher = {{Cambridge University Press}},
  author = {Boyd, Stephen and Vandenberghe, Lieven},
  year = {2004}
}

@article{breheny2011coordinate,
  title = {Coordinate Descent Algorithms for Nonconvex Penalized Regression, with Applications to Biological Feature Selection},
  volume = {5},
  timestamp = {2017-05-08T22:49:27Z},
  number = {1},
  journal = {The Annals of Applied Statistics},
  author = {Breheny, Patrick and Huang, Jian},
  year = {2011},
  pages = {232--253}
}

@article{strawderman1971proper,
  title={Proper {B}ayes minimax estimators of the multivariate normal mean},
  author={Strawderman, William E},
  journal={The Annals of Mathematical Statistics},
  volume={42},
  number={1},
  pages={385--388},
  year={1971},
  publisher={JSTOR}
}

@article{polson2010large,
  title = {Large-Scale Simultaneous Testing with Hypergeometric Inverted-Beta Priors},
  timestamp = {2017-05-08T22:49:27Z},
  journal = {arXiv preprint arXiv:1010.5223},
  author = {Polson, Nicholas G and Scott, James G},
  year = {2010}
}

@article{polson2015proximal,
  title = {Proximal Algorithms in Statistics and Machine Learning},
  volume = {30},
  timestamp = {2017-05-08T22:49:27Z},
  number = {4},
  journal = {Statistical Science},
  author = {Polson, Nicholas G and Scott, James G and Willard, Brandon T},
  year = {2015},
  pages = {559--581}
}

@article{strawderman2013hierarchical,
  title = {Hierarchical {{Bayes}}, Maximum a Posteriori Estimators, and Minimax Concave Penalized Likelihood Estimation},
  volume = {7},
  timestamp = {2017-05-08T22:49:27Z},
  journal = {Electronic Journal of Statistics},
  author = {Strawderman, Robert L and Wells, Martin T and Schifano, Elizabeth D},
  year = {2013},
  pages = {973--990}
}

@article{schifano2010majorization,
  title = {Majorization-Minimization Algorithms for Nonsmoothly Penalized Objective Functions},
  volume = {4},
  timestamp = {2017-05-08T22:49:27Z},
  journal = {Electronic Journal of Statistics},
  author = {Schifano, Elizabeth D and Strawderman, Robert L and Wells, Martin T},
  year = {2010},
  pages = {1258--1299}
}

@article{bhadra2015horseshoe+,
  title = {The {{Horseshoe}}+ {{Estimator}} of {{Ultra}}-{{Sparse Signals}}},
  author={Bhadra, Anindya and Datta, Jyotishka and Polson, Nicholas G and Willard, Brandon},
  journal={Bayesian Analysis},
  volume={12},
  number={4},
  pages={1105--1131},
  year={2017},
  publisher={International Society for Bayesian Analysis}
}

@article{bhadra2016prediction,
  title = {Prediction Risk for the Horseshoe Regression},
  journal = {Journal of Machine Learning Research},
  volume={20},
  number={78},
  pages={1--39},
  author = {Bhadra, Anindya and Datta, Jyotishka and Li, Yunfan and Polson, Nicholas G and Willard, Brandon},
  year = {2019}
}

@article{bhadra2015default,
  title={Default {B}ayesian analysis with global-local shrinkage priors},
  author={Bhadra, Anindya and Datta, Jyotishka and Polson, Nicholas G and Willard, Brandon},
  journal={Biometrika},
  volume={103},
  number={4},
  pages={955--969},
  year={2016},
  publisher={Oxford University Press}
}

@article{bhadra2016global,
  title = {Global-{{Local Mixtures}}},
  timestamp = {2017-05-08T22:49:27Z},
  journal = {Sankhya A, special issue in memory of Jayanta K.~Ghosh},
  volume={to appear},
  author = {Bhadra, Anindya and Datta, Jyotishka and Polson, Nicholas G and Willard, Brandon},
  year = {2019}
}

@article{bhadra2018inferring,
  title={Inferring network structure in non-normal and mixed discrete-continuous genomic data},
  author={Bhadra, Anindya and Rao, Arvind and Baladandayuthapani, Veerabhadran},
  journal={Biometrics},
  volume={74},
  number={1},
  pages={185--195},
  year={2018}
}

@article{bhadra2017expectation,
  title={An expectation--maximization scheme for measurement error models},
  author={Bhadra, Anindya},
  journal={Statistics \& Probability Letters},
  volume={120},
  pages={61--68},
  year={2017},
  publisher={North-Holland}
}


@article{bhadra2017horseshoe,
author = {Bhadra, Anindya and Datta, Jyotishka and Polson, Nicholas G and Willard, Brandon},
title = {{Horseshoe Regularization for Feature Subset Selection}},
journal = {Sankhya B, special issue in memory of Jayanta K. Ghosh},
year = {2019},
volume={to appear},
pages = {arXiv:1702.07400},
month = feb
}

@article{van2014horseshoe,
  title = {The Horseshoe Estimator: {{Posterior}} Concentration around Nearly Black Vectors},
  volume = {8},
  timestamp = {2017-05-08T22:49:27Z},
  journal = {Electronic Journal of Statistics},
  author = {{van der Pas}, SL and Kleijn, BJK and {van der Vaart}, AW},
  year = {2014},
  pages = {2585--2618}
}

@article{van2015conditions,
  title = {Conditions for {{Posterior Contraction}} in the {{Sparse Normal Means Problem}}},
  volume = {10},
  timestamp = {2017-05-08T22:49:27Z},
  journal = {Electronic Journal of Statistics},
  author = {{van der Pas}, St{\'e}phanie and Salomond, Jean-Bernard and Schmidt-Hieber, Johannes},
  year = {2016},
  pages = {976--1000}
}

@article{van2016many,
  title = {How Many Needles in the Haystack? {{Adaptive}} Inference and Uncertainty Quantification for the Horseshoe},
  timestamp = {2017-05-08T22:49:27Z},
  journal = {arXiv:1607.01892},
  author = {{van der Pas}, St{\'e}phanie and Szab{\'o}, Botond and {van der Vaart}, Aad},
  year = {2016}
}

@article{vanderpas2017,
author = "van der Pas, StÂphanie and Szab{\'o}, Botond and van der Vaart, Aad",
doi = "10.1214/17-BA1065",
journal = "Bayesian Analysis",
month = "12",
number = "4",
pages = "1221--1274",
publisher = "International Society for Bayesian Analysis",
title = "Uncertainty Quantification for the Horseshoe (with Discussion)",
url = "https://doi.org/10.1214/17-BA1065",
volume = "12",
year = "2017"
}


@article{van2017adaptive,
  title = {Adaptive Posterior Contraction Rates for the Horseshoe},
  timestamp = {2017-05-08T22:49:27Z},
  journal = {arXiv:1702.03698},
  author = {{van der Pas}, St{\'e}phanie and Szab{\'o}, Botond and der Vaart, van and {Aad}},
  year = {2017}
}

@article{datta2013asymptotic,
  title = {Asymptotic Properties of {{Bayes}} Risk for the Horseshoe Prior},
  volume = {8},
  timestamp = {2017-05-08T22:49:27Z},
  number = {1},
  journal = {Bayesian Analysis},
  author = {Datta, Jyotishka and Ghosh, Jayanta K},
  year = {2013},
  pages = {111--132}
}

@article{datta2016bayesian,
  title = {Bayesian Inference on Quasi-Sparse Count Data},
  volume = {103},
  timestamp = {2017-05-08T22:49:27Z},
  number = {4},
  journal = {Biometrika},
  author = {Datta, Jyotishka and Dunson, David B},
  year = {2016},
  pages = {971--983}
}

@article{donoho1992maximum,
  title = {Maximum Entropy and the Nearly Black Object},
  volume = {54},
  timestamp = {2017-05-08T22:49:27Z},
  journal = {Journal of the Royal Statistical Society. Series B},
  author = {Donoho, David L and Johnstone, Iain M and Hoch, Jeffrey C and Stern, Alan S},
  year = {1992},
  pages = {41--81}
}

@article{donoho1994ideal,
    AUTHOR = {Donoho, David L. and Johnstone, Iain M.},
     TITLE = {Ideal spatial adaptation by wavelet shrinkage},
   JOURNAL = {Biometrika},
  FJOURNAL = {Biometrika},
    VOLUME = {81},
      YEAR = {1994},
    NUMBER = {3},
     PAGES = {425--455},
      ISSN = {0006-3444},
   MRCLASS = {62G05},
  MRNUMBER = {1311089},
MRREVIEWER = {Ewaryst Rafaj\AA ÃÂowicz},
       DOI = {10.1093/biomet/81.3.425},
       URL = {http://0-dx.doi.org.library.uark.edu/10.1093/biomet/81.3.425},
}

@article{bhattacharya2014dirichlet,
  title = {Dirichlet-{{Laplace}} Priors for Optimal Shrinkage},
  volume = {110},
  timestamp = {2017-05-08T22:49:27Z},
  journal = {Journal of the American Statistical Association},
  author = {Bhattacharya, Anirban and Pati, Debdeep and Pillai, Natesh S. and Dunson, David B.},
  year = {2015},
  pages = {1479--1490}
}

@article{ghosh2016asymptotic,
author = "Ghosh, Prasenjit and Chakrabarti, Arijit",
doi = "10.1214/16-BA1029",
firstAvailable = "2016-09-30T20:19:35Z",
fjournal = "Bayesian Analysis",
handle = "euclid.ba/1475266758",
journal = "Bayesian Anal.",
note = "Advance publication",
publisher = "International Society for Bayesian Analysis",
title = "Asymptotic Optimality of One-Group Shrinkage Priors in Sparse High-dimensional Problems",
url = "http://dx.doi.org/10.1214/16-BA1029",
year = "2017"
}

@article{ghosh2016testing,
    AUTHOR = {Ghosh, Prasenjit and Tang, Xueying and Ghosh, Malay and
              Chakrabarti, Arijit},
     TITLE = {Asymptotic properties of {B}ayes risk of a general class of
              shrinkage priors in multiple hypothesis testing under
              sparsity},
   JOURNAL = {Bayesian Anal.},
  FJOURNAL = {Bayesian Analysis},
    VOLUME = {11},
      YEAR = {2016},
    NUMBER = {3},
     PAGES = {753--796},
      ISSN = {1936-0975},
   MRCLASS = {62F15 (62J15)},
  MRNUMBER = {3498045},
MRREVIEWER = {Monica Musio},
       DOI = {10.1214/15-BA973},
       URL = {http://0-dx.doi.org.library.uark.edu/10.1214/15-BA973},
}

@article{tiao1965bayesian,
    AUTHOR = {Tiao, George C. and Tan, W. Y.},
     TITLE = {Bayesian analysis of random-effect models in the analysis of
              variance. {II}. {E}ffect of autocorrelated errors},
   JOURNAL = {Biometrika},
  FJOURNAL = {Biometrika},
    VOLUME = {53},
      YEAR = {1966},
     PAGES = {477--495},
      ISSN = {0006-3444},
   MRCLASS = {62.35},
  MRNUMBER = {0214218},
MRREVIEWER = {D. A. Sprott},
       DOI = {10.2307/2333653},
       URL = {http://0-dx.doi.org.library.uark.edu/10.2307/2333653},
}
    

@article{morris2011estimating,
  title = {Estimating Random Effects via Adjustment for Density Maximization},
  volume = {26},
  timestamp = {2017-05-08T22:49:27Z},
  number = {2},
  journal = {Statistical Science},
  author = {Morris, Carl and Tang, Ruoxi and {others}},
  year = {2011},
  pages = {271--287}
}

@article{zou2005regularization,
  title = {Regularization and Variable Selection via the Elastic Net},
  volume = {67},
  timestamp = {2017-05-08T22:49:27Z},
  number = {2},
  journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
  author = {Zou, Hui and Hastie, Trevor},
  year = {2005},
  pages = {301--320}
}

@article{andrews1974scale,
    AUTHOR = {Andrews, D. F. and Mallows, C. L.},
     TITLE = {Scale mixtures of normal distributions},
   JOURNAL = {J. Roy. Statist. Soc. Ser. B},
  FJOURNAL = {Journal of the Royal Statistical Society. Series B.
              Methodological},
    VOLUME = {36},
      YEAR = {1974},
     PAGES = {99--102},
      ISSN = {0035-9246},
   MRCLASS = {62E15},
  MRNUMBER = {0359122},
MRREVIEWER = {M. E. Wise},
       URL =
              {http://0-links.jstor.org.library.uark.edu/sici?sici=0035-9246(1974)36:1<99:SMOND>2.0.CO;2-U&origin=MSN},
}

@article{piironen2016hyperprior,
  title = {On the {{Hyperprior Choice}} for the {{Global Shrinkage Parameter}} in the {{Horseshoe Prior}}},
  timestamp = {2017-05-08T22:49:27Z},
  journal = {arXiv preprint arXiv:1610.05559},
  author = {Piironen, Juho and Vehtari, Aki},
  year = {2016}
}

@article{ghosal2000,
  title = {Convergence Rates of Posterior Distributions},
  volume = {28},
  timestamp = {2017-05-08T22:49:27Z},
  number = {2},
  url = {http://dx.doi.org/10.1214/aos/1016218228},
  journal = {The Annals of Statistics},
  author = {Ghosal, Subhashis and Ghosh, Jayanta K. and {van der Vaart}, Aad W.},
  month = apr,
  year = {2000},
  pages = {500--531}
}

@article{wang2013class,
  title = {On a Class of Shrinkage Priors for Covariance Matrix Estimation},
  volume = {22},
  timestamp = {2017-05-08T22:49:27Z},
  number = {3},
  journal = {Journal of Computational and Graphical Statistics},
  author = {Wang, Hao and Pillai, Natesh S},
  year = {2013},
  pages = {689--707}
}

@article{sabetsarvestani_sparse_2014,
  title = {Sparse {{Estimation}} with {{Generalized Beta Mixture}} and the {{Horseshoe Prior}}},
  timestamp = {2017-05-08T22:49:27Z},
  urldate = {2016-11-12},
  url = {http://search.arxiv.org:8081/paper.jsp?r=1411.2405\&qid=1478992712845ler_nCnN_47610269\&qs=horseshoe},
  author = {Sabetsarvestani, Zahra and Amindavar, Hamidreza},
  month = nov,
  year = {2014}
}

@article{scott_parameter_2010,
  title = {Parameter Expansion in Local-Shrinkage Models},
  timestamp = {2017-05-08T22:49:27Z},
  urldate = {2015-06-13},
  url = {http://arxiv.org/abs/1010.5265},
  journal = {arXiv preprint arXiv:1010.5265},
  author = {Scott, James G.},
  year = {2010},
}

@article{makalic_simple_2016,
  title = {A Simple Sampler for the Horseshoe Estimator},
  volume = {23},
  issn = {1070-9908, 1558-2361},
  timestamp = {2017-05-08T22:49:27Z},
  archivePrefix = {arXiv},
  eprinttype = {arxiv},
  eprint = {1508.03884},
  number = {1},
  urldate = {2016-11-12},
  url = {http://arxiv.org/abs/1508.03884},
  journal = {IEEE Signal Processing Letters},
  author = {Makalic, Enes and Schmidt, Daniel F.},
  month = jan,
  year = {2016},
  keywords = {-,Computation,Statistics},
  pages = {179--182}
}

@article{bhattacharya_fast_2015,
  title={Fast sampling with {G}aussian scale mixture priors in high-dimensional regression},
  author={Bhattacharya, Anirban and Chakraborty, Antik and Mallick, Bani K},
  journal={Biometrika},
  volume={103},
  pages={985--991},
  year={2016},
  publisher={Oxford University Press}
}

@techreport{hahn_elliptical_2016,
  title = {Elliptical Slice Sampling for {{Bayesian}} Shrinkage Regression with Applications to Causal Inference},
  timestamp = {2017-05-08T22:49:27Z},
  urldate = {2016-09-02},
  url = {http://faculty.chicagobooth.edu/richard.hahn/JCGS_submit.pdf},
  author = {Hahn, P. Richard and He, Jingyu and Lopes, Hedibert},
  year = {2016},
  keywords = {horseshoe prior,sparsity}
}

@article{hans2011elastic,
  title = {Elastic Net Regression Modeling with the Orthant Normal Prior},
  volume = {106},
  timestamp = {2017-05-08T22:49:27Z},
  number = {496},
  journal = {Journal of the American Statistical Association},
  author = {Hans, Chris},
  year = {2011},
  pages = {1383--1393}
}

@article{tseng2001convergence,
  title = {Convergence of a Block Coordinate Descent Method for Nondifferentiable Minimization},
  volume = {109},
  timestamp = {2017-05-08T22:49:27Z},
  number = {3},
  journal = {Journal of optimization theory and applications},
  author = {Tseng, Paul},
  year = {2001},
  pages = {475--494}
}

@article{fu1998penalized,
  title = {Penalized Regressions: The Bridge versus the Lasso},
  volume = {7},
  timestamp = {2017-05-08T22:49:27Z},
  number = {3},
  journal = {Journal of computational and graphical statistics},
  author = {Fu, Wenjiang J},
  year = {1998},
  pages = {397--416}
}

@article{tibshirani2014praise,
  title = {In Praise of Sparsity and Convexity},
  timestamp = {2017-05-08T22:49:27Z},
  journal = {Past, Present, and Future of Statistical Science},
  author = {Tibshirani, Robert J},
  year = {2014},
  pages = {497--505}
}

@article{friedman2010regularization,
  title = {Regularization Paths for Generalized Linear Models via Coordinate Descent},
  volume = {33},
  timestamp = {2017-05-08T22:49:27Z},
  number = {1},
  journal = {Journal of Statistical Software},
  author = {Friedman, Jerome and Hastie, Trevor and Tibshirani, Rob},
  year = {2010},
  pages = {1-22}
}

@article{gramacy2010shrinkage,
  title = {Shrinkage Regression for Multivariate Inference with Missing Data, and an Application to Portfolio Balancing},
  volume = {5},
  timestamp = {2017-05-08T22:49:27Z},
  number = {2},
  journal = {Bayesian Analysis},
  author = {Gramacy, Robert B and Pantaleo, Ester and {others}},
  year = {2010},
  pages = {237--262}
}

@article{makalic2016high,
  title = {High-{{Dimensional Bayesian Regularised Regression}} with the {{BayesReg Package}}},
  timestamp = {2017-05-08T22:49:27Z},
  journal = {arXiv preprint arXiv:1611.06649},
  author = {Makalic, Enes and Schmidt, Daniel F},
  year = {2016}
}

@article{james2017scalable,
  title = {Scalable {MCMC} for {B}ayes Shrinkage Priors},
  timestamp = {2017-05-08T22:49:27Z},
  journal = {arXiv preprint arXiv:1705.00841},
  author = {Johndrow, James E. and Orenstein, Paulo},
  year = {2017}
}

@article{terenin_gpu-accelerated_2016,
  title = {{{GPU}}-Accelerated {{Gibbs Sampling}}},
  timestamp = {2017-05-08T22:49:27Z},
  archivePrefix = {arXiv},
  eprinttype = {arxiv},
  eprint = {1608.04329},
  urldate = {2016-11-12},
  url = {http://arxiv.org/abs/1608.04329},
  journal = {arXiv:1608.04329 [cs, stat]},
  author = {Terenin, Alexander and Dong, Shawfeng and Draper, David},
  month = aug,
  year = {2016},
  keywords = {and Cluster Computing,Computer Science - Distributed,Parallel,Statistics - Computation}
}

@Manual{pas_horseshoe:_2016,
    title = {horseshoe: Implementation of the Horseshoe Prior},
    author = {Stephanie {van der Pas} and James Scott and Antik Chakraborty and Anirban Bhattacharya},
    year = {2016},
    note = {R package version 0.1.0},
    url = {https://CRAN.R-project.org/package=horseshoe},
  }
@article{zou2006adaptive,
  title = {The Adaptive Lasso and Its Oracle Properties},
  volume = {101},
  timestamp = {2017-05-08T22:49:27Z},
  number = {476},
  journal = {Journal of the American statistical association},
  author = {Zou, Hui},
  year = {2006},
  pages = {1418--1429}
}

@article{donoho2006compressed,
  title = {Compressed Sensing},
  volume = {52},
  timestamp = {2017-05-08T22:49:27Z},
  number = {4},
  journal = {IEEE Transactions on information theory},
  author = {Donoho, David L},
  year = {2006},
  pages = {1289--1306}
}

@article{candes2008restricted,
  title = {The Restricted Isometry Property and Its Implications for Compressed Sensing},
  volume = {346},
  timestamp = {2017-05-08T22:49:27Z},
  number = {9-10},
  journal = {Comptes Rendus Mathematique},
  author = {Candes, Emmanuel J},
  year = {2008},
  pages = {589--592}
}

@article{tibshirani2011solution,
   AUTHOR = {Tibshirani, Ryan J. and Taylor, Jonathan},
     TITLE = {The solution path of the generalized lasso},
   JOURNAL = {Ann. Statist.},
  FJOURNAL = {The Annals of Statistics},
    VOLUME = {39},
      YEAR = {2011},
    NUMBER = {3},
     PAGES = {1335--1371},
      ISSN = {0090-5364},
   MRCLASS = {62J07},
  MRNUMBER = {2850205},
MRREVIEWER = {Pierre Alquier},
       DOI = {10.1214/11-AOS878},
       URL = {http://0-dx.doi.org.library.uark.edu/10.1214/11-AOS878},
}

@article{friedman2008sparse,
  title = {Sparse Inverse Covariance Estimation with the Graphical Lasso},
  volume = {9},
  timestamp = {2017-05-08T22:49:27Z},
  number = {3},
  journal = {Biostatistics},
  author = {Friedman, Jerome and Hastie, Trevor and Tibshirani, Robert},
  year = {2008},
  pages = {432--441}
}

@article{yuan2006model,
  title = {Model Selection and Estimation in Regression with Grouped Variables},
  volume = {68},
  timestamp = {2017-05-08T22:49:27Z},
  number = {1},
  journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
  author = {Yuan, Ming and Lin, Yi},
  year = {2006},
  pages = {49--67}
}

@article{mazumder2010spectral,
  title = {Spectral Regularization Algorithms for Learning Large Incomplete Matrices},
  volume = {11},
  timestamp = {2017-05-08T22:49:27Z},
  number = {Aug},
  journal = {Journal of machine learning research},
  author = {Mazumder, Rahul and Hastie, Trevor and Tibshirani, Robert},
  year = {2010},
  pages = {2287--2322}
}

@article{jolliffe2003modified,
  title = {A Modified Principal Component Technique Based on the {{LASSO}}},
  volume = {12},
  timestamp = {2017-05-08T22:49:27Z},
  number = {3},
  journal = {Journal of computational and Graphical Statistics},
  author = {Jolliffe, Ian T and Trendafilov, Nickolay T and Uddin, Mudassir},
  year = {2003},
  pages = {531--547}
}

@article{witten2009penalized,
 author = {Witten, Daniela M. and Tibshirani, Robert and Hastie, Trevor},
title = {A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis},
journal = {Biostatistics},
volume = {10},
number = {3},
pages = {515-534},
year = {2009},
doi = {10.1093/biostatistics/kxp008},
URL = { + http://dx.doi.org/10.1093/biostatistics/kxp008},
eprint = {/oup/backfile/content_public/journal/biostatistics/10/3/10.1093_biostatistics_kxp008/1/kxp008.pdf}
}

@article{tibshirani2011nearly,
  title = {Nearly-Isotonic Regression},
  volume = {53},
  timestamp = {2017-05-08T22:49:27Z},
  number = {1},
  journal = {Technometrics},
  author = {Tibshirani, Ryan J and Hoefling, Holger and Tibshirani, Robert},
  year = {2011},
  pages = {54--61}
}

@article{belloni2011square,
  title = {Square-Root Lasso: Pivotal Recovery of Sparse Signals via Conic Programming},
  volume = {98},
  timestamp = {2017-05-08T22:49:27Z},
  number = {4},
  journal = {Biometrika},
  author = {Belloni, Alexandre and Chernozhukov, Victor and Wang, Lie},
  year = {2011},
  pages = {791--806}
}

@article{sun2012scaled,
  title = {Scaled Sparse Linear Regression},
  volume = {99},
  timestamp = {2017-05-08T22:49:27Z},
  number = {4},
  journal = {Biometrika},
  author = {Sun, Tingni and Zhang, Cun-Hui},
  year = {2012},
  pages = {879--898}
}

@article{candes2007dantzig,
  title = {The {{Dantzig}} Selector: {{Statistical}} Estimation When p Is Much Larger than N},
  timestamp = {2017-05-08T22:49:27Z},
  journal = {The Annals of Statistics},
  author = {Candes, Emmanuel and Tao, Terence},
  year = {2007},
  pages = {2313--2351}
}

@article{candes2010power,
  title = {The Power of Convex Relaxation: {{Near}}-Optimal Matrix Completion},
  volume = {56},
  timestamp = {2017-05-08T22:49:27Z},
  number = {5},
  journal = {IEEE Transactions on Information Theory},
  author = {Cand{\`e}s, Emmanuel J and Tao, Terence},
  year = {2010},
  pages = {2053--2080}
}

@inproceedings{peltola2014hierarchical,
  title = {Hierarchical {{Bayesian}} Survival Analysis and Projective Covariate Selection in Cardiovascular Event Risk Prediction},
  timestamp = {2017-05-08T22:49:27Z},
  booktitle = {Proceedings of the {{Eleventh UAI Conference}} on {{Bayesian Modeling Applications Workshop}}-{{Volume}} 1218},
  publisher = {{CEUR-WS. org}},
  author = {Peltola, Tomi and Havulinna, Aki S and Salomaa, Veikko and Vehtari, Aki},
  year = {2014},
  pages = {79--88}
}

@book{wald1950statistical,
  title={Statistical Decision Functions.},
  author={Wald, Abraham},
  year={1950},
  publisher={Wiley, New York}
}


@inproceedings{stein_inadmissibility_1956,
  title = {Inadmissibility of the Usual Estimator for the Mean of a Multivariate Normal Distribution},
  volume = {1},
  timestamp = {2017-05-08T23:17:29Z},
  urldate = {2017-05-08},
  url = {http://www.stat.yale.edu/~hz68/619/Stein-1956.pdf},
  booktitle = {Proceedings of the {{Third Berkeley}} Symposium on Mathematical Statistics and Probability},
  author = {Stein, Charles},
  year = {1956},
  pages = {197--206}
}
@article{hahn_shrinkage_2014,
  title = {Shrinkage priors for linear instrumental variable models with many instruments},
  journal={arXiv preprint arXiv:1408.0462},
  author = {Hahn, P. Richard and Lopes, Hedibert},
  month = aug,
  year = {2014}
}

@inproceedings{ingraham_bayesian_2016,
  title = {Bayesian {Sparsity} for {Intractable} {Distributions}},
  author = {Ingraham, John B. and Marks, Debora S.},
booktitle={International Conference on Machine Learning},
  year={2017}
}

@article{piironen_projection_2015,
  title = {Projection predictive variable selection using {Stan}+{R}},
  url = {http://search.arxiv.org:8081/paper.jsp?r=1508.02502&qid=1478992712845ler_nCnN_47610269&qs=horseshoe},
  urldate = {2016-11-12},
  author = {Piironen, Juho and Vehtari, Aki},
  month = aug,
    journal={arXiv preprint arXiv:1508.02502},
  year={2015}
}


@article{besag1986statistical,
  title={On the statistical analysis of dirty pictures},
  author={Besag, Julian},
  journal={Journal of the Royal Statistical Society: Series B (Methodological)},
  volume={48},
  number={3},
  pages={259--279},
  year={1986},
  publisher={Wiley Online Library}
}


@article{magnusson_dolda_2016,
  title = {{DOLDA} - a regularized supervised topic model for high-dimensional multi-class regression},
  url = {http://search.arxiv.org:8081/paper.jsp?r=1602.00260&qid=1478992712845ler_nCnN_47610269&qs=horseshoe},
  urldate = {2016-11-12},
  author = {Magnusson, MÃÂÃÂ¥ns and Jonsson, Leif and Villani, Mattias},
  month = jan,
  journal={arXiv preprint arXiv:1602.00260},
  year={2016}
}

@article{bhadra_prediction_2016,
  title = {Prediction risk for global-local shrinkage regression},
  url = {http://arxiv.org/abs/1605.04796},
  urldate = {2016-07-16},
  journal = {arXiv preprint arXiv:1605.04796},
  author = {Bhadra, Anindya and Datta, Jyotishka and Li, Yunfan and Polson, Nicholas G. and Willard, Brandon},
  year = {2016}
}

@article{berzuini_mendelian_2016,
  title = {Mendelian {Randomization} with {Poor} {Instruments}: a {Bayesian} {Approach}},
  shorttitle = {Mendelian {Randomization} with {Poor} {Instruments}},
  url = {http://arxiv.org/abs/1608.02990},
  urldate = {2016-11-12},
  journal = {arXiv:1608.02990 [math, stat]},
  author = {Berzuini, Carlo and Guo, Hui and Burgess, Stephen and Bernardinelli, Luisa},
  month = aug,
  year = {2016},
  note = {arXiv: 1608.02990},
  keywords = {Mathematics - Statistics Theory, Statistics - Methodology}
}

@article{faulkner_bayesian_2015,
  title = {Bayesian trend filtering: adaptive temporal smoothing with shrinkage priors},
  shorttitle = {Bayesian trend filtering},
  url = {https://arxiv.org/abs/1512.06505},
  urldate = {2016-11-12},
  author = {Faulkner, James R. and Minin, Vladimir N.},
  month = dec,
  year = {2015}
}
@inproceedings{hernandez-lobato_learning_2013,
  title = {Learning feature selection dependencies in multi-task learning},
  url = {http://papers.nips.cc/paper/5212-learning-feature-selection-dependencies-in-multi-task-learning},
  urldate = {2016-11-12},
  booktitle = {Advances in {Neural} {Information} {Processing} {Systems}},
  author = {HernÃÂÃÂ¡ndez-Lobato, Daniel and HernÃÂÃÂ¡ndez-Lobato, JosÃÂÃÂ© Miguel},
  year = {2013},
  pages = {746--754}
}

