Bayesian model selection based on parameter estimates from subsamples

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. [Copyright &y& Elsevier]