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MCMC using Markov bases for computing $$p$$ -values in decomposable log-linear models

Author

Listed:
  • Masahiro Kuroda
  • Hiroki Hashiguchi
  • Shigekazu Nakagawa
  • Zhi Geng

Abstract

We derive an explicit form of a Markov basis on the junction tree for a decomposable log-linear model. Then we give a description of a Markov basis characterized by global Markov properties associated with the graph of a decomposable log-linear model and show how to use the Markov basis for generating contingency tables of a Markov chain. The estimates of exact $$p$$ -values can be obtained from contingency tables generated from the proposed Markov chain Monte Carlo using the Markov basis. Copyright Springer-Verlag 2013

Suggested Citation

  • Masahiro Kuroda & Hiroki Hashiguchi & Shigekazu Nakagawa & Zhi Geng, 2013. "MCMC using Markov bases for computing $$p$$ -values in decomposable log-linear models," Computational Statistics, Springer, vol. 28(2), pages 831-850, April.
    • Handle: RePEc:spr:compst:v:28:y:2013:i:2:p:831-850
    DOI: 10.1007/s00180-012-0331-3
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    References listed on IDEAS

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    1. Jones, Galin L. & Haran, Murali & Caffo, Brian S. & Neath, Ronald, 2006. "Fixed-Width Output Analysis for Markov Chain Monte Carlo," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1537-1547, December.
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