Bayesian Model Averaging and Bayesian Predictive Information Criterion for Model Selection
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- Ando Tomohiro
- Graduate School of Business Administration, Keio University
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The problem of evaluating the goodness of the predictive distributions developed by the Bayesian model averaging approach is investigated. Considering the maximization of the posterior mean of the expected log-likelihood of the predictive distributions (Ando (2007a)), we develop the Bayesian predictive information criterion (BPIC). According to the numerical examples, we show that the posterior mean of the log-likelihood has a positive bias comparing with the posterior mean of the expected log-likelihood, and that the bias estimate of BPIC is close to the true bias. One of the advantages of BPIC is that we can optimize the size of Occam's razor. Monte Carlo simulation results show that the proposed method performs well.
収録刊行物
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- JOURNAL OF THE JAPAN STATISTICAL SOCIETY
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JOURNAL OF THE JAPAN STATISTICAL SOCIETY 38 (2), 243-257, 2008
日本統計学会
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詳細情報 詳細情報について
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- CRID
- 1390001205286401408
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- NII論文ID
- 110006835625
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- NII書誌ID
- AA1105098X
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- ISSN
- 13486365
- 18822754
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- MRID
- 2458930
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- NDL書誌ID
- 9597266
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- NDL
- Crossref
- CiNii Articles
- KAKEN
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- 抄録ライセンスフラグ
- 使用不可