2010年6月
Optimal tuning parameter estimation in maximum penalized likelihood method
ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS
- ,
- 巻
- 62
- 号
- 3
- 開始ページ
- 413
- 終了ページ
- 438
- 記述言語
- 英語
- 掲載種別
- DOI
- 10.1007/s10463-008-0186-0
- 出版者・発行元
- SPRINGER HEIDELBERG
In maximum penalized or regularized methods, it is important to select a tuning parameter appropriately. This paper proposes a direct plug-in method for tuning parameter selection. The tuning parameters selected using a generalized information criterion (Konishi and Kitagawa, Biometrika, 83, 875-890, 1996) and cross-validation (Stone, Journal of the Royal Statistical Society, Series B, 58, 267-288, 1974) are shown to be asymptotically equivalent to those selected using the proposed method, from the perspective of estimation of an optimal tuning parameter. Because of its directness, the proposed method is superior to the two selection methods mentioned above in terms of computational cost. Some numerical examples which contain the penalized spline generalized linear model regressions are provided.
- リンク情報
- ID情報
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- DOI : 10.1007/s10463-008-0186-0
- ISSN : 0020-3157
- Web of Science ID : WOS:000276161700001