Sieve M-estimation for semiparametric varying-coefficient partially linear regression model
Sieve M-estimation for semiparametric varying-coefficient partially linear regression model作者机构：School of Mathematical Sciences Beijing Normal University Laboratory of Mathematics and Complex Systems Ministry of Education Beijing China School of Mathematical Sciences Capital Normal University Beijing China
出 版 物：《Science China Mathematics》 (中国科学：数学（英文版）)
年 卷 期：2010年第53卷第8期
页 面：1995-2010页
核心收录：
学科分类：02[经济学] 0202[经济学-应用经济学] 020208[经济学-统计学] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 070103[理学-概率论与数理统计] 0701[理学-数学]
基 金：supported by Natural Natural Science Foundation of China (Grant Nos.10771017,10901020) Key Project of Chinese Ministry of Education (Grant No.309007)
主 题：partly linear model varying-coefficient robustness optimal convergence rate asymptotic normality
摘 要：This article considers a semiparametric varying-coefficient partially linear regression *** semiparametric varying-coefficient partially linear regression model which is a generalization of the partially linear regression model and varying-coefficient regression model that allows one to explore the possibly nonlinear effect of a certain covariate on the response variable.A sieve M-estimation method is proposed and the asymptotic properties of the proposed estimators are *** main object is to estimate the nonparametric component and the unknown parameters *** is easier to compute and the required computation burden is much less than the existing two-stage estimation ***,the sieve M-estimation is robust in the presence of outliers if we choose appropriate ρ(·).Under some mild conditions,the estimators are shown to be strongly consistent;the convergence rate of the estimator for the unknown nonparametric component is obtained and the estimator for the unknown parameter is shown to be asymptotically normally *** experiments are carried out to investigate the performance of the proposed method.
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