Predictive density estimation under the Wasserstein loss

被引：5

作者：

Matsuda, Takeru ^{[1
,2
]}

Strawderman, William E. ^{[3
]}

机构：

[1] Univ Tokyo, Grad Sch Informat Sci & Technol, Tokyo, Japan

[2] RIKEN, Ctr Brain Sci, Saitama, Japan

[3] Rutgers State Univ, Dept Stat & Biostat, New Brunswick, NJ USA

来源：

JOURNAL OF STATISTICAL PLANNING AND INFERENCE | 2021年 / 210卷

关键词：

Optimal transport; Predictive density; Shrinkage estimation; Wasserstein distance; BAYES MINIMAX ESTIMATORS; VARIANCE; VECTORS;

D O I：

10.1016/j.jspi.2020.05.005

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

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. Simulation results are also presented. (C) 2020 Elsevier B.V. All rights reserved.

引用

页码：53 / 63

页数：11

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