Bayesian model selection based on parameter estimates from subsamples

Jingsi Zhang*, Wenxin Jiang, Xiaofeng Shao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

We propose Bayesian model selection based on composite datasets, which can be constructed from various subsample estimates. The method remains consistent without fully specifying a probability model, and is useful for dependent data, when asymptotic variance of the parameter estimator is difficult to estimate.

Original languageEnglish (US)
Pages (from-to)979-986
Number of pages8
JournalStatistics and Probability Letters
Volume83
Issue number4
DOIs
StatePublished - Apr 2013

Keywords

  • Bayes factor
  • Consistency
  • Model selection
  • Schwarz's Bayesian information criterion (BIC)
  • Self-normalization

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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