@article{d7814f3970d44b9c839c90d0083c171c,
title = "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.",
keywords = "Bayes factor, Consistency, Model selection, Schwarz's Bayesian information criterion (BIC), Self-normalization",
author = "Jingsi Zhang and Wenxin Jiang and Xiaofeng Shao",
note = "Funding Information: We thank the Associate Editor (and the referee) for useful suggestions that have improved the paper. The first author was supported by the State Scholarship Fund of China during her visit in the US, when part of this work was completed. The second author thanks Qilu Securities Institute for Financial Studies, Shandong University, for the hospitality during his visit, where part of this work was done. The third author acknowledges partial financial support from National Science Foundation grants DMS-0804937 and DMS-1104545 .",
year = "2013",
month = apr,
doi = "10.1016/j.spl.2012.12.020",
language = "English (US)",
volume = "83",
pages = "979--986",
journal = "Statistics and Probability Letters",
issn = "0167-7152",
publisher = "Elsevier",
number = "4",
}