Sampling for Conditional Inference on Contingency Tables

Robert D. Eisinger, Yuguo Chen

Research output: Contribution to journalArticlepeer-review


We propose new sequential importance sampling methods for sampling contingency tables with given margins. The proposal for each method is based on asymptotic approximations to the number of tables with fixed margins. These methods generate tables that are very close to the uniform distribution. The tables, along with their importance weights, can be used to approximate the null distribution of test statistics and calculate the total number of tables. We apply the methods to a number of examples and demonstrate an improvement over other methods in a variety of real problems. Supplementary materials are available online.

Original languageEnglish (US)
Pages (from-to)79-87
Number of pages9
JournalJournal of Computational and Graphical Statistics
Issue number1
StatePublished - Jan 2 2017


  • Counting problem
  • Monte Carlo method
  • Sequential importance sampling

ASJC Scopus subject areas

  • Statistics and Probability
  • Discrete Mathematics and Combinatorics
  • Statistics, Probability and Uncertainty


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