期刊
STATISTICS IN MEDICINE
卷 35, 期 14, 页码 2441-2454出版社
WILEY
DOI: 10.1002/sim.6897
关键词
accelerated failure time model; flexible errors; model selection; multivariate; noninformative prior; skewness
类别
资金
- EPSRC [EP/K007521/1]
- King Abdullah University of Science and Technology (KAUST)
- EPSRC [EP/K007521/1] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/K007521/1] Funding Source: researchfish
We study Bayesian linear regression models with skew-symmetric scale mixtures of normal error distributions. These kinds of models can be used to capture departures from the usual assumption of normality of the errors in terms of heavy tails and asymmetry. We propose a general noninformative prior structure for these regression models and show that the corresponding posterior distribution is proper under mild conditions. We extend these propriety results to cases where the response variables are censored. The latter scenario is of interest in the context of accelerated failure time models, which are relevant in survival analysis. We present a simulation study that demonstrates good frequentist properties of the posterior credible intervals associated with the proposed priors. This study also sheds some light on the trade-off between increased model flexibility and the risk of over-fitting. We illustrate the performance of the proposed models with real data. Although we focus on models with univariate response variables, we also present some extensions to the multivariate case in the Supporting Information. Copyright (C) 2016 JohnWiley & Sons, Ltd.
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