Optimal tuning parameter estimation in maximum penalized likelihood method
Optimal tuning parameter estimation in maximum penalized likelihood method
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DOI：
10.1007/s10463-008-0186-0
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发表时间：
2010-06
影响因子：
1
通讯作者：
Masao Ueki;K. Fueda
中科院分区：
文献类型：
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作者：
Masao Ueki;K. Fueda
在最大惩罚或正则化方法中，适当地选择调整参数是很重要的。本文提出了一种直接插入式整定参数选择方法。从估计最优调谐参数的角度来看，使用广义信息准则（Konishi和Kitagawa，Biometrika，83，875-890，1996）和交叉验证（Stone，皇家统计学会杂志，系列B，58，267-288，1974）选择的调谐参数被示出为渐近地等效于使用所提出的方法选择的那些调谐参数。由于其直接性，所提出的方法是上级优于上述两种选择方法的计算成本。给出了惩罚样条广义线性模型回归的数值算例。
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.