Asymptotic Properties of Wavelet Estimators in Partially Linear Errors-in-variables Models with Long-memory Errors

作     者：Hong-chang HU Heng-jian CUI Kai-can LI 

作者机构：School of Mathematics and Statistics Hubei Normal University Huangshi 435002 China School of Mathematical science Capital Normal University Beijing 100048 China 

出 版 物：《Acta Mathematicae Applicatae Sinica》 (应用数学学报（英文版）)

年 卷 期：2018年第34卷第1期

页      面：77-96页

核心收录：

学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学] 

基　　金：Supported by the National Natural Science Foundation of China(No.11471105,11471223) Scientific Research Item of Education Office,Hubei(No.D20172501) 

主　　题：partially linear errors-in-variables model nonlinear long dependent time series wavelet estimation asymptotic representation asymptotic distribution weak convergence rates 

摘      要：While the random errors are a function of Gaussian random variables that are stationary and long dependent, we investigate a partially linear errors-in-variables（EV） model by the wavelet method. Under general conditions, we obtain asymptotic representation of the parametric estimator, and asymptotic distributions and weak convergence rates of the parametric and nonparametric estimators. At last, the validity of the wavelet method is illuminated by a simulation example and a real example.