Semiparametric approximation methods in multivariate model selection

被引：5

作者：

Gao, J ^{[1
]}

Wolff, R

Anh, V

机构：

[1] Univ Western Australia, Dept Math & Stat, Nedlands, WA 6907, Australia

[2] Queensland Univ Technol, Sch Math Sci, Brisbane, Qld 4001, Australia

来源：

JOURNAL OF COMPLEXITY | 2001年 / 17卷 / 04期

基金：

澳大利亚研究理事会;

关键词：

dimensional reduction; linear regression; model selection; nonlinear regression; nonlinear time series; nonparametric regression; semiparametric regression;

D O I：

10.1006/jcom.2001.0591

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

In this paper we propose a cross-validation selection criterion to determine asymptotically the correct model among the family of all possible partially linear models when the underlying model is a partially linear model. We establish the asymptotic consistency of the criterion. In addition, the criterion is illustrated using two real sets of data. (C) 2001 Academic Press.

引用

页码：754 / 772

页数：19

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