@article{aitkin1935least,
  title={On least squares and linear combination of observations},
  author={Aitkin, AC},
  journal={Proceedings of the RSE},
  volume={55},
  pages={42--48},
  year={1935}
}
@article{10.2307/2284231,
 ISSN = {01621459},
 URL = {http://www.jstor.org/stable/2284231},
 abstract = {The first part of this article considers the Bayesian problem of estimating the mean, θ, of a normal distribution when the mean itself has a normal prior. The usual Bayes estimator for this situation has high risk if θ is far from the mean of the prior distribution. We suggest rules which do not have this bad property and still perform well against the normal prior. These rules are compromises between the Bayes rule and the MLE. Similar rules are suggested for the empirical Bayes situation where the mean and variance of the prior is unknown but can be estimated from the data provided by several simultaneous estimation problems. In this case the suggested rules compromise between the James-Stein estimator of a mean vector and the MLE.},
 author = {Bradley Efron and Carl Morris},
 journal = {Journal of the American Statistical Association},
 number = {336},
 pages = {807--815},
 publisher = {[American Statistical Association, Taylor & Francis, Ltd.]},
 title = {Limiting the Risk of Bayes and Empirical Bayes Estimators--Part I: The Bayes Case},
 volume = {66},
 year = {1971}
}


@article{10.2307/2284711,
 ISSN = {01621459},
 URL = {http://www.jstor.org/stable/2284711},
 abstract = {We discuss compromises between Stein's estimator and the MLE which limit the risk to individual components of the estimation problem while sacrificing only a small fraction of the savings in total squared error loss given by Stein's rule. The compromise estimators "limit translation" away from the MLE. The calculations are pursued in an empirical Bayesian manner by considering their performance against an entire family of prior distributions on the unknown parameters.},
 author = {Bradley Efron and Carl Morris},
 journal = {Journal of the American Statistical Association},
 number = {337},
 pages = {130--139},
 publisher = {[American Statistical Association, Taylor & Francis, Ltd.]},
 title = {Limiting the Risk of Bayes and Empirical Bayes Estimators--Part II: The Empirical Bayes Case},
 volume = {67},
 year = {1972}
}
@article{10.2307/2284155,
 ISSN = {01621459},
 URL = {http://www.jstor.org/stable/2284155},
 abstract = {Stein's estimator for k normal means is known to dominate the MLE if k ≥ 3. In this article we ask if Stein's estimator is any good in its own right. Our answer is yes: the positive part version of Stein's estimator is one member of a class of "good" rules that have Bayesian properties and also dominate the MLE. Other members of this class are also useful in various situations. Our approach is by means of empirical Bayes ideas. In the later sections we discuss rules for more complicated estimation problems, and conclude with results from empirical linear Bayes rules in non-normal cases.},
 author = {Bradley Efron and Carl Morris},
 journal = {Journal of the American Statistical Association},
 number = {341},
 pages = {117--130},
 publisher = {[American Statistical Association, Taylor & Francis, Ltd.]},
 title = {Stein's Estimation Rule and Its Competitors--An Empirical Bayes Approach},
 volume = {68},
 year = {1973}
}
@article{Casella85,
 ISSN = {00031305},
 URL = {http://www.jstor.org/stable/2682801},
 abstract = {Empirical Bayes methods have been shown to be powerful data-analysis tools in recent years. The empirical Bayes model is much richer than either the classical or the ordinary Bayes model and often provides superior estimates of parameters. An introduction to some empirical Bayes methods is given, and these methods are illustrated with two examples.},
 author = {George Casella},
 journal = {The American Statistician},
 number = {2},
 pages = {83--87},
 publisher = {[American Statistical Association, Taylor & Francis, Ltd.]},
 title = {An Introduction to Empirical Bayes Data Analysis},
 volume = {39},
 year = {1985}
}
@Book{ nla.cat-vn712791,
author = { Bickel, Peter J. and Doksum, Kjell A},
title = { Mathematical statistics : basic ideas and selected topics / Peter J. Bickel, Kjell A. Doksum },
isbn = { 0816207844 },
publisher = { Holden-Day San Francisco },
pages = { ix, 492 p. : },
year = { 1977 },
type = { Book },
language = { English },
subjects = { Mathematical statistics. },
life-dates = { 1977 -  },
catalogue-url = { https://nla.gov.au/nla.cat-vn712791 },
}
@misc{meir2021generaldomain,
      title={General-Domain Truth Discovery via Average Proximity}, 
      author={Reshef Meir and Ofra Amir and Omer Ben-Porat and Tsviel Ben-Shabat and Gal Cohensius and Lirong Xia},
      year={2021},
      eprint={1905.00629},
      archivePrefix={arXiv},
      primaryClass={cs.AI}
}
@inproceedings{10.1145/3366423.3380250,
author = {Braylan, Alexander and Lease, Matthew},
title = {Modeling and Aggregation of Complex Annotations via Annotation Distances},
year = {2020},
isbn = {9781450370233},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3366423.3380250},
doi = {10.1145/3366423.3380250},
booktitle = {Proceedings of The Web Conference 2020},
pages = {1807–1818},
numpages = {12},
location = {Taipei, Taiwan},
series = {WWW '20}
}
@article {Parisi1253,
	author = {Parisi, Fabio and Strino, Francesco and Nadler, Boaz and Kluger, Yuval},
	title = {Ranking and combining multiple predictors without labeled data},
	volume = {111},
	number = {4},
	pages = {1253--1258},
	year = {2014},
	doi = {10.1073/pnas.1219097111},
	publisher = {National Academy of Sciences},
	issn = {0027-8424},
	URL = {https://www.pnas.org/content/111/4/1253},
	eprint = {https://www.pnas.org/content/111/4/1253.full.pdf},
	journal = {Proceedings of the National Academy of Sciences}
}
@article{grofman1983thirteen,
	title={Thirteen theorems in search of the truth},
	author={Grofman, Bernard and Owen, Guillermo and Feld, Scott L},
	journal={Theory and Decision},
	volume={15},
	number={3},
	pages={261--278},
	year={1983},
	publisher={Springer}
}
@article{dawid1979maximum,
  title={Maximum likelihood estimation of observer error-rates using the EM algorithm},
  author={Dawid, Alexander Philip and Skene, Allan M},
  journal={Journal of the Royal Statistical Society: Series C (Applied Statistics)},
  volume={28},
  number={1},
  pages={20--28},
  year={1979},
  publisher={Wiley Online Library}
}
@techreport{stein1956inadmissibility,
  title={Inadmissibility of the usual estimator for the mean of a multivariate normal distribution},
  author={Stein, Charles},
  year={1956},
  institution={Stanford University Stanford United States}
}
@incollection{robbins1992empirical,
  title={An empirical Bayes approach to statistics},
  author={Robbins, Herbert E},
  booktitle={Breakthroughs in statistics},
  pages={388--394},
  year={1992},
  publisher={Springer}
}
@incollection{james1961estimation,
  title={Estimation with quadratic loss},
  author={James, William and Stein, Charles},
  booktitle={Breakthroughs in statistics},
  publisher={Springer},
  year={1961}
}
@Book{pointestimation,
author={Lehmann, Erich L and Casella, George},
title = { Theory of Point Estimation, Second Edition / George Casella, Stephan Fienberg, Ingram Olkin},
isbn = { 0-387-98502-6 },
publisher = { Springer },
pages = { 366 - 370, 408 problem 6.1},
year = { 1998 },
type = { Book },
language = { English },
subjects = { Mathematical statistics. }
}


@book{diebold1998elements,
  title={Elements of forecasting},
  author={Diebold, Francis X},
  year={1998},
  publisher={South-Western College Pub.}
}
@article{GTM,
  title={A probabilistic model for estimating real-valued truth from conflicting sources},
  author={Zhao, Bo and Han, Jiawei},
  journal={Proc. of QDB},
  volume={1817},
  year={2012},
  publisher={Citeseer}
}
@article{CATD,
  title={A confidence-aware approach for truth discovery on long-tail data},
  author={Li, Qi and Li, Yaliang and Gao, Jing and Su, Lu and Zhao, Bo and Demirbas, Murat and Fan, Wei and Han, Jiawei},
  journal={Proceedings of the VLDB Endowment},
  volume={8},
  number={4},
  pages={425--436},
  year={2014},
  publisher={VLDB Endowment}
}
@inproceedings{CRH,
  title={Resolving conflicts in heterogeneous data by truth discovery and source reliability estimation},
  author={Li, Qi and Li, Yaliang and Gao, Jing and Zhao, Bo and Fan, Wei and Han, Jiawei},
  booktitle={SIGMOD'14},
  year={2014}
}
@inproceedings{KDEm,
  title={From truth discovery to trustworthy opinion discovery: An uncertainty-aware quantitative modeling approach},
  author={Wan, Mengting and Chen, Xiangyu and Kaplan, Lance and Han, Jiawei and Gao, Jing and Zhao, Bo},
  booktitle={SIGKDD'16},
  pages={1885--1894},
  year={2016}
}
@article{survey,
	title={A survey on truth discovery},
	author={Li, Yaliang and Gao, Jing and Meng, Chuishi and Li, Qi and Su, Lu and Zhao, Bo and Fan, Wei and Han, Jiawei},
	journal={ACM SIGKDD Explorations Newsletter},
	volume={17},
	number={2},
	pages={1--16},
	year={2016},
	publisher={ACM}
}
@inproceedings{snow2008cheap,
  title={Cheap and fast--but is it good? evaluating non-expert annotations for natural language tasks},
  author={Snow, Rion and O’connor, Brendan and Jurafsky, Dan and Ng, Andrew Y},
  booktitle={Proceedings of the 2008 conference on empirical methods in natural language processing},
  pages={254--263},
  year={2008}
}
@article{hart2018statistical,
  title={The statistical shape of geometric reasoning},
  author={Hart, Yuval and Dillon, Moira R and Marantan, Andrew and Cardenas, Anna L and Spelke, Elizabeth and Mahadevan, L},
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  volume={8},
  number={1},
  pages={12906},
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  publisher={Nature Publishing Group}
}
@article{voxPopuli,
  title={Vox Populi},
  author={Francis Galton},
  journal={Nature},
  volume={75},
  number={1949},
  pages={450-451},
  year={1907},
  publisher={Nature Publishing Group}
}