期刊
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
卷 10, 期 3, 页码 581-591出版社
AMER STATISTICAL ASSOC
DOI: 10.1198/106186001317114992
关键词
Kullback-Leibler loss; penalized likelihood; smoothing parameter
This article presents an alternative derivation of the generalized approximate cross-validation (GACV) score of Xiang and Wahba (1996) for smoothing parameter selection in penalized likelihood regression. The new derivation suggests a simple numerical solution that is stable for all sample sizes. Also suggested is a variant of the score that can be computationally more convenient. Simple simulations are presented to illustrate the effectiveness of the scores.
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