A difference-based approach in the partially linear model with dependent errors

被引：6

作者：

Zeng, Zhen ^{[1
]}

Liu, Xiangdong ^{[1
]}

机构：

[1] Jinan Univ, Dept Stat, Guangzhou, Guangdong, Peoples R China

来源：

JOURNAL OF INEQUALITIES AND APPLICATIONS | 2018年

基金：

中国国家自然科学基金;

关键词：

NSD random variables; Partially linear model; Asymptotic normality; Finite difference; Least square; WEIGHTED SUMS; COMPLETE CONVERGENCE; WAVELET ESTIMATORS; REGRESSION-MODELS; RANDOM-VARIABLES;

D O I：

10.1186/s13660-018-1857-x

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

We study asymptotic properties of estimators of parameter and non-parameter in a partially linear model in which errors are dependent. Using a difference-based and ordinary least square (DOLS) method, the estimator of an unknown parametric component is given and the asymptotic normality of the DOLS estimator is obtained. Meanwhile, the estimator of a nonparametric component is derived by the wavelet method, and asymptotic normality and the weak convergence rate of the wavelet estimator are discussed. Finally, the performance of the proposed estimator is evaluated by a simulation study.

引用

页数：16

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