Asymptotic properties for the estimators in heteroscedastic semiparametric EV models with α-mixing errors

被引：2

作者：

Xi, Mengmei ^{[1
]}

Wang, Rui ^{[1
]}

Yu, Wei ^{[1
]}

Shen, Yan ^{[1
]}

Wang, Xuejun ^{[1
]}

机构：

[1] Anhui Univ, Sch Math Sci, Hefei 230601, Peoples R China

来源：

STATISTICS | 2020年 / 54卷 / 06期

基金：

中国国家自然科学基金;

关键词：

Semiparametric EV regression model; alpha-mixing errors; least squares estimator; strong consistency; PARTIAL LINEAR-MODELS; COMPLETE CONVERGENCE; RANDOM-VARIABLES; WEIGHTED SUMS; REGRESSION; NORMALITY; CONSISTENCY;

D O I：

10.1080/02331888.2020.1867857

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

In this paper, the heteroscedastic semiparametric errors-in-variables (EV) model, yi = xi(i)beta + g(t(i)) + epsilon(i), x(i) = xi(i) + mu(i), 1 <= i <= n, is considered, where epsilon(i) = sigma(i)e(i), sigma(2)(i) = f (u(i)), beta is an unknown parameter to be estimated and g(center dot) and f (center dot) are unknown functions to be estimated. Under some suitable conditions, asymptotic properties for the estimators of beta, g(center dot) and f (center dot) are presented based on alpha-mixing random errors. In addition, finite sample behavior of the estimators is provided via simulations to verify the validity of the theoretical results.

引用

页码：1232 / 1254

页数：23

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