Bayesian model selection based on parameter estimates from subsamples
Wenxin Jiang and
Xiaofeng Shao ()
Statistics & Probability Letters, 2013, vol. 83, issue 4, 979-986
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.
Keywords: Bayes factor; Consistency; Model selection; Schwarz’s Bayesian information criterion (BIC); Self-normalization (search for similar items in EconPapers)
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