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

Fingerprint

Saddlepoint
Prior distribution
Maximum likelihood
Predictors
Empirical Bayes Method
Deviance Information Criterion
Posterior Mean
Maximum Likelihood Estimator
Partial

All Science Journal Classification (ASJC) codes

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

Cite this

Saddlepoint condition on a predictor to reconfirm the need for the assumption of a prior distribution. / Yanagimoto, Takemi; Ohnishi, Toshio.

In: Journal of Statistical Planning and Inference, Vol. 141, No. 5, 01.05.2011, p. 1990-2000.

Research output: Contribution to journalArticle

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