Change point estimation in high dimensional Markov random-field models

Sandipan Roy

Abstract:

The paper investigates a change point estimation problem in the context of high
dimensional Markov random-field models. Change points represent a key feature in many dynamically
evolving network structures. The change point estimate is obtained by maximizing a
profile penalized pseudolikelihood function under a sparsity assumption.We also derive a tight
bound for the estimate, up to a logarithmic factor, even in settings where the number of possible
edges in the network far exceeds the sample size. The performance of the estimator proposed
is evaluated on synthetic data sets and is also used to explore voting patterns in the US Senate
in the 1979–2012 period.