Journal
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
Volume 51, Issue 4, Pages 1591-1615Publisher
TAYLOR & FRANCIS INC
DOI: 10.1080/03610918.2019.1676439
Keywords
Covariance matrices; Linear mixed-effects models; Model selection criteria; Vague priors; 62PXX
Categories
Funding
- Tertiary Education Trust Fund (TETFund)-AS&D grant of the Federal government of Nigeria through the Federal University of Agriculture, Abeokuta Nigeria
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In the study of using vague priors for the covariance parameters of random effects, the marginal criteria outperform the conditional criteria, and joint and separation priors outperform the inverse-Wishart prior in all scenarios.
We explore the performance of three popular Bayesian model-selection criteria when vague priors are used for the covariance parameters of the random effects in a linear mixed-effects model (LMM) using an extensive simulation study. In a previous paper, we have shown that the conditional selection criteria perform worse than their marginal counterparts. It is known that for some ?vague? priors, their impact on the estimated model parameters can be non-negligible, e.g., for the priors of the covariance matrix of the random effects in a longitudinal LMM. We evaluate here the impact of vague priors for the covariance matrix of the random effects on selecting the correct LMM using classical Bayesian selection criteria. We consider marginal and conditional criteria. For the random intercept case, we assign different vague priors to the variance parameters. With two or more random effects, we considered five different specifications of inverse-Wishart (IW) prior, five different separation priors and a joint prior. The results show again the better performance of the marginal over the conditional criteria and the superiority of joint and separation priors over IW in all settings. We also illustrate the performance of the selection criteria on a practical dataset.
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