Paper Info
Title
Simultaneous mean and covariance estimation of partially linear models for longitudinal data with missing responses and covariate measurement error.
Abstract
Missing responses and covariate measurement error are very commonly seen in practice. New estimating equations are developed to simultaneously estimate the mean and covariance under a partially linear model for longitudinal data with missing responses and covariate measurement error. Specifically, a novel approach is proposed to handle measurement error by using independent replicate measurements. Compared with existing methods, the proposed method requires fewer assumptions. For example, it does not require to specify the distribution of the mismeasured covariate or the measurement error, and does not need a parametric model to estimate the probability of being observed or to impute the missing responses. Additionally, the proposed estimating equations are easy to implement in most popular statistical softwares by applying existing algorithms for standard generalized estimating equations. The asymptotic properties of the proposed estimators are established under regularity conditions, and simulation studies demonstrate desired properties. Finally, the proposed method is applied to data from the Lifestyle Education for Activity and Nutrition (LEAN) study. This data analysis confirms the effectiveness of the intervention in producing weight loss at month nine.
Year
DOI
Venue
2016
10.1016/j.csda.2015.11.001
Computational Statistics & Data Analysis
Keywords
Field
DocType
Longitudinal data,Measurement error,Missing data,Partially linear models
Econometrics,Covariate,Estimation of covariance matrices,Linear model,Missing data,Statistics,Mathematics,Observational error,Estimating equations,Covariance,Estimator
Journal
Volume
Issue
ISSN
96
C
0167-9473
Citations 
PageRank 
References 
2
0.89
0
Authors
3
Name
Order
Citations
PageRank
Guoyou Qin121.23
Jiajia Zhang22616.64
Zhong Yi Zhu33410.77