| Autor: |
Matsuda, Takeru, Strawderman, William E. |
| Rok vydání: |
2019 |
| Předmět: |
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| Zdroj: |
Journal of Statistical Planning and Inference, 210, 53--63, 2021 |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1016/j.jspi.2020.05.005 |
| Popis: |
We investigate predictive density estimation under the $L^2$ Wasserstein loss for location families and location-scale families. We show that plug-in densities form a complete class and that the Bayesian predictive density is given by the plug-in density with the posterior mean of the location and scale parameters. We provide Bayesian predictive densities that dominate the best equivariant one in normal models. |
| Databáze: |
arXiv |
| Externí odkaz: |
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