Rejection control and sequential importance sampling

Jun S. Liu, Rong Chen, Wing Hung Wong

Research output: Contribution to journalArticlepeer-review

81 Scopus citations

Abstract

We discuss ways of combining rejection sampling and importance sampling methods in Monte Carlo computations and demonstrate their usefulness in updating dynamic systems. Specifically, we propose the rejection controlled sequential importance sampling (RC-SIS) algorithm, which is designed to simultaneously reduce Monte Carlo variation and retain independent samples in sequential importance sampling. The proposed method is demonstrated by three examples taken from econometrics, hierarchical Bayes analysis, and digital telecommunications. They all show significant improvements over previous results.

Original languageEnglish (US)
Pages (from-to)1022-1031
Number of pages10
JournalJournal of the American Statistical Association
Volume93
Issue number443
DOIs
StatePublished - Sep 1 1998
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Keywords

  • Blind deconvolution
  • Econometric disequilibrium model
  • Gibbs sampler
  • Markov chain Monte Carlo
  • Markov chain Monte Carlo
  • Sequential imputation
  • Weighted Markov chain Monte Carlo

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