Strong consistency of estimators for heteroscedastic partly linear regression model under dependent samples
type: Item
instance of: scholarly article
title: Strong consistency of estimators for heteroscedastic partly linear regression model under dependent samples
zbMATH Open document ID: 1014.62046
DOI: 10.1155/S1048953302000187
author: Han-Ying Liang
author: Bing-Yi Jing
published in: Journal of Applied Mathematics and Stochastic Analysis
publication date: 2003-07-13T00:00:00Z
review text: Summary: We are concerned with the heteroscedastic regression model \(y_i=x_i \beta+ g(t_i)+ \sigma_ie_i\), \(1\leq i\leq n,\) under correlated errors \(e_i\), where it is assumed that \(\sigma^2_i= f(u_i)\), the design points \((x_i,t_i, u_i)\) are known and nonrandom, and \(g\) and \(f\) are unknown functions. The interest lies in the slope parameter \(\beta\). Assuming the unobserved disturbances \(e_i\) are negatively associated, we study the issue of strong consistency for two different slope estimators: the least squares estimator and the weighted least squares estimator.
Mathematics Subject Classification ID: 62G08
Mathematics Subject Classification ID: 62G05
zbMATH DE Number: 1910670
zbMATH Keywords: partial linear model
zbMATH Keywords: negatively associated samples
zbMATH Keywords: weighted least squares estimator
zbMATH Keywords: strong consistency
MaRDI profile type: Publication
full work available at URL: s1048953302000187
OpenAlex ID: W2069568641
description: scientific article; zbMATH DE number 1910670