Bayesian analysis of two-piece location-scale models under reference priors with partial information
Document Type
Article
Publication Date
4-2016
Department
Department of Mathematical Sciences
Abstract
Bayesian estimators are developed and compared with the maximum likelihood estimators for the two-piece location-scale models, which contain several well-known distributions such as the asymmetric Laplace distribution, the two-piece normal distribution, and the two-piece Student-t distribution. For the validity of Bayesian analysis, it is essential to use priors that could lead to proper posterior distributions. Specifically, reference priors with partial information have been considered. A sufficient and necessary condition is established to guarantee the propriety of the posterior distribution under a general class of priors. The performance of the proposed approach is illustrated through extensive simulation studies and real data analysis.
Publication Title
Computational Statistics and Data Analysis
Recommended Citation
Tu, S.,
Wang, M.,
&
Sun, X.
(2016).
Bayesian analysis of two-piece location-scale models under reference priors with partial information.
Computational Statistics and Data Analysis,
96, 133-144.
http://doi.org/10.1016/j.csda.2015.11.002
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/6273