Bayesian predictive information criterion for the evaluation of hierarchical Bayesian and empirical Bayes models

Tomohiro Ando

研究成果: Article査読

142 被引用数 (Scopus)

抄録

The problem of evaluating the goodness of the predictive distributions of hierarchical Bayesian and empirical Bayes models is investigated. A Bayesian predictive information criterion is proposed as an estimator of the posterior mean of the expected loglikelihood of the predictive distribution when the specified family of probability distributions does not contain the true distribution. The proposed criterion is developed by correcting the asymptotic bias of the posterior mean of the loglikelihood as an estimator of its expected loglikelihood. In the evaluation of hierarchical Bayesian models with random effects, regardless of our parametric focus, the proposed criterion considers the bias correction of the posterior mean of the marginal loglikelihood because it requires a consistent parameter estimator. The use of the bootstrap in model evaluation is also discussed.

本文言語English
ページ(範囲)443-458
ページ数16
ジャーナルBiometrika
94
2
DOI
出版ステータスPublished - 2007 6月

ASJC Scopus subject areas

  • 統計学および確率
  • 数学 (全般)
  • 農業および生物科学(その他)
  • 農業および生物科学(全般)
  • 統計学、確率および不確実性
  • 応用数学

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