Bayesian analysis of hierarchical heteroscedastic linear models using Dirichlet-Laplace priors

Bayesian analysis of hierarchical heteroscedastic linear models using Dirichlet-Laplace priors

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From practical point Food Storage Container Sets of view, in a two-level hierarchical model, the variance of second-level usually has a tendency to change through sub-populations.The existence of this kind of local (or intrinsic ) heteroscedasticity is a major concern in the application of statistical modeling.The main purpose of this study is to construct a Bayesian methodology via shrinkage priors in order to estimate the interesting parameters under local heteroscedasticity.The suggested methodology for this issue is to use of a class of the local-global shrinkage priors, called COQ10 Dirichlet-Laplace priors.The optimal posterior concentration and straightforward posterior computation are the appealing properties of these priors.

Two real data sets are analyzed to illustrate the proposed methodology.