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Variable Selection for Bayesian Linear Regression Model in a Finite Sample Size
http://hdl.handle.net/2241/118425
http://hdl.handle.net/2241/1184258456bf11-2f01-430d-a85b-52860f424f4c
名前 / ファイル | ライセンス | アクション |
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1303.pdf (496.6 kB)
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Item type | Technical Report(1) | |||||
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公開日 | 2013-02-28 | |||||
タイトル | ||||||
言語 | en | |||||
タイトル | Variable Selection for Bayesian Linear Regression Model in a Finite Sample Size | |||||
言語 | ||||||
言語 | eng | |||||
資源タイプ | ||||||
資源 | http://purl.org/coar/resource_type/c_18gh | |||||
タイプ | technical report | |||||
アクセス権 | ||||||
アクセス権 | open access | |||||
アクセス権URI | http://purl.org/coar/access_right/c_abf2 | |||||
著者 |
KABE, Satoshi
× KABE, Satoshi× 金澤, 雄一郎 |
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抄録 | ||||||
内容記述タイプ | Abstract | |||||
内容記述 | In Bayesian data analysis, a deviance information criterion (DIC) proposed by Spiegelhalter et al. (2002) is widely used for the model selection, since this criterion is relatively easy to calculate and applicable to a wide range of statistical models. Spiegelhalter et al. (2002) gave an asymptotic justification of DIC in the case where the number of observations grows with respect to the number of parameters. In small-sample cases, however, the estimated asymptotic bias of DIC might underestimate the true bias (Burnham, 2002). In this paper, we propose a finite-sample bias corrected information criterion (ICBL) for the Bayesian linear regression models with conjugate priors, as AICC proposed by Sugiura (1978) in frequentist framework. We examine the performance of the proposed information criterion relative to the DIC for small-sample cases by simulation, and found that our proposed information criterion outperforms DIC. |
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言語 | en | |||||
書誌情報 |
発行日 2013-02 |
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出版者 | ||||||
言語 | en | |||||
出版者 | University of Tsukuba. Graduate School of Systems and Information Engineering. Doctoral Program in Social Systems & Management | |||||
シリーズ | ||||||
言語 | en | |||||
関連名称 | Department of Social Systems and Management Discussion Paper Series;no.1303 |