Optimal shrinkage estimation in heteroscedastic hierarchical linear models
Fuente:
arXiv
Guardado en:
| Autores principales: | , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2015
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866910493652811776 |
|---|---|
| author | Kou, Samuel Yang, Justin J. |
| author_facet | Kou, Samuel Yang, Justin J. |
| contents | Shrinkage estimators have profound impacts in statistics and in scientific and engineering applications. In this article, we consider shrinkage estimation in the presence of linear predictors. We formulate two heteroscedastic hierarchical regression models and study optimal shrinkage estimators in each model. A class of shrinkage estimators, both parametric and semiparametric, based on unbiased risk estimate (URE) is proposed and is shown to be (asymptotically) optimal under mean squared error loss in each model. Simulation study is conducted to compare the performance of the proposed methods with existing shrinkage estimators. We also apply the method to real data and obtain encouraging and interesting results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1503_06262 |
| institution | arXiv |
| publishDate | 2015 |
| record_format | arxiv |
| spellingShingle | Optimal shrinkage estimation in heteroscedastic hierarchical linear models Kou, Samuel Yang, Justin J. Methodology 62J07 (Primary), 62F12 (Secondary) Shrinkage estimators have profound impacts in statistics and in scientific and engineering applications. In this article, we consider shrinkage estimation in the presence of linear predictors. We formulate two heteroscedastic hierarchical regression models and study optimal shrinkage estimators in each model. A class of shrinkage estimators, both parametric and semiparametric, based on unbiased risk estimate (URE) is proposed and is shown to be (asymptotically) optimal under mean squared error loss in each model. Simulation study is conducted to compare the performance of the proposed methods with existing shrinkage estimators. We also apply the method to real data and obtain encouraging and interesting results. |
| title | Optimal shrinkage estimation in heteroscedastic hierarchical linear models |
| topic | Methodology 62J07 (Primary), 62F12 (Secondary) |
| url | https://arxiv.org/abs/1503.06262 |