Bayesian prediction of a density function in terms of e-mixture

Takemi Yanagimoto, Toshio Ohnishi

研究成果: ジャーナルへの寄稿学術誌査読

6 被引用数 (Scopus)

抄録

The optimum Bayesian predictor under the e-divergence loss is proposed and discussed. Notable dualistic structure is observed between the proposed predictor and the optimum predictor under the m-divergence loss, the latter of which is dominantly discussed in the existing literature. An advantage of the proposed optimum predictor is that it is estimative, when the sampling density is in the exponential family. Potential advantages of the proposed predictor over its dual one are discussed, which include the shrinkage estimator and the Bayesian model selection criterion DIC (deviance information criterion). Further, we emphasize potential usefulness of the use of Jeffreys' prior.

本文言語英語
ページ(範囲)3064-3075
ページ数12
ジャーナルJournal of Statistical Planning and Inference
139
9
DOI
出版ステータス出版済み - 9月 1 2009
外部発表はい

!!!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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