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Bayesian model selection based on parameter estimates from subsamples

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  • Zhang, Jingsi
  • Jiang, Wenxin
  • Shao, Xiaofeng

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.

Suggested Citation

  • Zhang, Jingsi & Jiang, Wenxin & Shao, Xiaofeng, 2013. "Bayesian model selection based on parameter estimates from subsamples," Statistics & Probability Letters, Elsevier, vol. 83(4), pages 979-986.
    • Handle: RePEc:eee:stapro:v:83:y:2013:i:4:p:979-986
    DOI: 10.1016/j.spl.2012.12.020
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    References listed on IDEAS

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