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Modfiied Conditional AIC in Linear Mixed Models
http://hdl.handle.net/2261/55381
http://hdl.handle.net/2261/55381c749b6ef-69ce-4478-af61-981ec7ca9fdf
| Item type | テクニカルレポート / Technical Report(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2013-09-17 | |||||
| タイトル | ||||||
| タイトル | Modfiied Conditional AIC in Linear Mixed Models | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Asymptotically unbiased estimator | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Akaike information criterion | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | conditional AIC | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Kullback-Leibler information | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | linear mixed model | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | small area estimation | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | variable selection | |||||
| 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 | metadata only access | |||||
| アクセス権URI | http://purl.org/coar/access_right/c_14cb | |||||
| 著者 |
Kawakubo, Yuki
× Kawakubo, Yuki× Kubokawa, Tatsuya |
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| 著者所属 | ||||||
| 著者所属 | University of Tokyo | |||||
| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | In linear mixed models, the conditional Akaike Information Criterion (cAIC) is a procedure for variable selection in light of the prediction of specific clusters or random effects. This is useful in problems involving prediction of random effects such as small area estimation, and much attention has been received since suggested by Vaida and Blanchard (2005). A weak point of cAIC is that it is derived as an unbiased estimator of conditional Akaike information (cAI) in the overspecified case, namely in the case that candidate models include the true model. This results in larger biases in the underspecified case that the true model is not included in candidate models. In this paper, we derive the modified cAIC (McAIC) to cover both the underspecified and overspecified cases, and investigate properties of McAIC. It is numerically shown that McAIC has less biases and less prediction errors than cAIC. | |||||
| 内容記述 | ||||||
| 内容記述タイプ | Other | |||||
| 内容記述 | 本文フィルはリンク先を参照のこと | |||||
| 書誌情報 |
Discussion paper series. CIRJE-F 巻 CIRJE-F-895, 発行日 2013-07 |
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| 書誌レコードID | ||||||
| 収録物識別子タイプ | NCID | |||||
| 収録物識別子 | AA11450569 | |||||
| フォーマット | ||||||
| 内容記述タイプ | Other | |||||
| 内容記述 | application/pdf | |||||
| 出版者 | ||||||
| 出版者 | 日本経済国際共同センター | |||||
| 出版者別名 | ||||||
| Center for International Research on the Japanese Economy | ||||||
| 関係URI | ||||||
| 識別子タイプ | URI | |||||
| 関連識別子 | http://www.cirje.e.u-tokyo.ac.jp/research/dp/2013/2013cf895ab.html | |||||
