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

Takemi Yanagimoto, Toshio Ohnishi

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)1990-2000
Number of pages11
JournalJournal of Statistical Planning and Inference
Volume141
Issue number5
DOIs
Publication statusPublished - May 1 2011

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

Fingerprint Dive into the research topics of 'Saddlepoint condition on a predictor to reconfirm the need for the assumption of a prior distribution'. Together they form a unique fingerprint.

  • Cite this