Conditional inference given partial information in contingency tables using Markov bases

Research output: Contribution to journalReview article

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Abstract

In this article, we review a Markov chain Monte Carlo (MCMC) algorithm for performing conditional inference in contingency tables in the presence of partial information using Markov bases, a key tool arising from the area known as algebraic statistics. We review applications of this algorithm to the problems of conditional exact tests, ecological inference, and disclosure limitation and illustrate how these problems fall naturally in the setting of inference with partial information. We also discuss some issues associated with computing Markov bases which are needed as an input to the algorithm.

Original languageEnglish (US)
Pages (from-to)207-218
Number of pages12
JournalWiley Interdisciplinary Reviews: Computational Statistics
Volume5
Issue number3
DOIs
StatePublished - May 1 2013

All Science Journal Classification (ASJC) codes

  • Statistics and Probability

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