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.
- Bayes factor
- Model selection
- Schwarz's Bayesian information criterion (BIC)
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty