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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