Practical variable selection for generalized additive models

作     者：Marra, Giampiero Wood, Simon N. 

作者机构：UCL Dept Stat Sci London WC1E 6BT England Univ Bath Dept Math Sci Bath BA2 7AY Avon England 

出 版 物：《COMPUTATIONAL STATISTICS & DATA ANALYSIS》 (计算统计学与数据分析)

年 卷 期：2011年第55卷第7期

页      面：2372-2387页

核心收录：

学科分类：08[工学] 0714[理学-统计学（可授理学、经济学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Generalized additive model Nonnegative garrote estimator Penalized thin plate regression spline Practical variable selection Shrinkage smoother 

摘      要：The problem of variable selection within the class of generalized additive models, when there are many covariates to choose from but the number of predictors is still somewhat smaller than the number of observations, is considered. Two very simple but effective shrinkage methods and an extension of the nonnegative garrote estimator are introduced. The proposals avoid having to use nonparametric testing methods for which there is no general reliable distributional theory. Moreover, component selection is carried out in one single step as opposed to many selection procedures which involve an exhaustive search of all possible models. The empirical performance of the proposed methods is compared to that of some available techniques via an extensive simulation study. The results show under which conditions one method can be preferred over another, hence providing applied researchers with some practical guidelines. The procedures are also illustrated analysing data on plasma beta-carotene levels from a cross-sectional study conducted in the United States. (C) 2011 Elsevier B.V. All rights reserved.