Saddlepoint condition on a predictor to reconfirm the need for the assumption of a prior distribution

Takemi Yanagimoto, Toshio Ohnishi

研究成果: ジャーナルへの寄稿学術誌査読

1 被引用数 (Scopus)

抄録

Saddlepoint conditions on a predictor are introduced and developed to reconfirm the need for the assumption of a prior distribution in constructing a useful inferential procedure. A condition yields that the predictor induced from the maximum likelihood estimator is the worst under a loss, while the predictor induced from a suitable posterior mean is the best. This result indicates the promising role of Bayesian criteria, such as the deviance information criterion (DIC). As an implication, we critique the conventional empirical Bayes method because of its partial assumption of a prior distribution.

本文言語英語
ページ(範囲)1990-2000
ページ数11
ジャーナルJournal of Statistical Planning and Inference
141
5
DOI
出版ステータス出版済み - 5月 2011

!!!All Science Journal Classification (ASJC) codes

  • 統計学および確率
  • 統計学、確率および不確実性
  • 応用数学

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