Bayesian analysis of hierarchical heteroscedastic linear models using Dirichlet-Laplace priors
From practical point 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 purpo...
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| Format: | Article |
| Language: | English |
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Springer
2017-02-01
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| Series: | Journal of Statistical Theory and Applications (JSTA) |
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| Online Access: | https://www.atlantis-press.com/article/25872954.pdf |
| Summary: | From practical point 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 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. |
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| ISSN: | 1538-7887 |