文章基本信息

标题：Dimension reduction and parameter estimation for additive index models
本地全文：下载
作者：Lingyan Ruan ; Ming Yuan
期刊名称：Statistics and Its Interface
印刷版ISSN：1938-7989
电子版ISSN：1938-7997
出版年度：2010
卷号：3
期号：4
页码：493-499
DOI：10.4310/SII.2010.v3.n4.a7
出版社：International Press
摘要：In this paper, we consider simultaneous model selection and estimation for the additive index model. The additive index model is a class of structured nonparametric models that can be expressed as additive models of a set of unknown linear transformation of the original predictor variables. We introduce a penalized least squares estimator and discuss how it can be efficiently computed in practice. Both theoretical and empirical properties of the estimate are presented to demonstrate its merits. Extensions to more general prediction framework are also discussed.
关键词：additive model; index model; model selection; projection pursuit; smoothing splines