Strong consistency of estimators in a partially linear model with asymptotically almost negatively associated errors
type: Item
instance of: scholarly article
title: Strong consistency of estimators in a partially linear model with asymptotically almost negatively associated errors
zbMATH Open document ID: 1459.62072
DOI: 10.1155/2020/2934914
author: Yu Zhang
author: Xinsheng Liu
author: Mohamed Sief
published in: Discrete Dynamics in Nature and Society
publication date: 2020-10-14T00:00:00Z
review text: Summary: This paper studies a heteroscedastic partially linear regression model in which the errors are asymptotically almost negatively associated (AANA, in short) random variables with not necessarily identical distribution and zero mean. Under some mild conditions, we establish the strong consistency of least squares estimators, weighted least squares estimators, and the ultimate weighted least squares estimators for the unknown parameter, respectively. In addition, the strong consistency of the estimator for nonparametric component is also investigated. The results derived in the paper include the corresponding ones of independent random errors and some dependent random errors as special cases. At last, two simulations are carried out to study the numerical performance of the strong consistency for least squares estimators and weighted least squares estimators of the unknown parametric and nonparametric components in the model.
Mathematics Subject Classification ID: 62G20
Mathematics Subject Classification ID: 62J05
zbMATH DE Number: 7260903
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2020/2934914
OpenAlex ID: W3085066906
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description: scientific article; zbMATH DE number 7260903