Stochastic approximation Monte Carlo importance sampling for approximating exact conditional probabilities
- Authors
- Cheon, Sooyoung; Liang, Faming; Chen, Yuguo; Yu, Kai
- Issue Date
- Jul-2014
- Publisher
- SPRINGER
- Keywords
- Contingency table; Exact inference; Importance sampling; MCMC; Stochastic approximation; Monte Carlo
- Citation
- STATISTICS AND COMPUTING, v.24, no.4, pp.505 - 520
- Indexed
- SCIE
SCOPUS
- Journal Title
- STATISTICS AND COMPUTING
- Volume
- 24
- Number
- 4
- Start Page
- 505
- End Page
- 520
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/98161
- DOI
- 10.1007/s11222-013-9384-6
- ISSN
- 0960-3174
- Abstract
- Importance sampling and Markov chain Monte Carlo methods have been used in exact inference for contingency tables for a long time, however, their performances are not always very satisfactory. In this paper, we propose a stochastic approximation Monte Carlo importance sampling (SAMCIS) method for tackling this problem. SAMCIS is a combination of adaptive Markov chain Monte Carlo and importance sampling, which employs the stochastic approximation Monte Carlo algorithm (Liang et al., J. Am. Stat. Assoc., 102(477):305-320, 2007) to draw samples from an enlarged reference set with a known Markov basis. Compared to the existing importance sampling and Markov chain Monte Carlo methods, SAMCIS has a few advantages, such as fast convergence, ergodicity, and the ability to achieve a desired proportion of valid tables. The numerical results indicate that SAMCIS can outperform the existing importance sampling and Markov chain Monte Carlo methods: It can produce much more accurate estimates in much shorter CPU time than the existing methods, especially for the tables with high degrees of freedom.
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Collections - Graduate School > Department of Applied Statistics > 1. Journal Articles
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