Robust estimation in linear regression models for longitudinal data with covariate measurement errors and outliers
type: Item
instance of: scholarly article
title: Robust estimation in linear regression models for longitudinal data with covariate measurement errors and outliers
zbMATH Open document ID: 1408.62131
author: Yuexia Zhang
author: Guoyou Qin
author: Jiajia Zhang
author: Zhongyi Zhu
published in: Journal of Multivariate Analysis
publication date: 2018-10-16T00:00:00Z
review text: In this paper, a robust estimating equation method to estimate the linear regression model for longitudinal data with covariate measurement errors and outliers is proposed. First, the independence between replicate measurements is used to reduce the biases caused by measurement errors. Then centralization of the observed covariate matrix is used to correct the biases caused by outliers. The method does not require specifying the distributions of the true covariates, response and measurement errors. Standard generalized estimating equations algorithms are used to implement it. Asymptotic normality of the proposed estimator under regularity conditions is established in the paper, which also includes simulation studies showing that the proposed method performs better in handling measurement errors and outliers than other existing methods. An application of the proposed method to a real data set from the Lifestyle Education for Activity and Nutrition (LEAN) study is also presented.
reviewed by: Annibal Parracho Sant'Anna
Mathematics Subject Classification ID: 62J05
Mathematics Subject Classification ID: 62F35
Mathematics Subject Classification ID: 62P10
zbMATH DE Number: 6955877
zbMATH Keywords: estimating equation
zbMATH Keywords: longitudinal data
zbMATH Keywords: measurement error
zbMATH Keywords: outlier
MaRDI profile type: Publication
full work available at URL: j.jmva.2018.07.015
OpenAlex ID: W2886612075
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Wikidata QID: Q129477852
DOI: 10.1016/J.JMVA.2018.07.015
description: scientific article; zbMATH DE number 6955877