Premium
Variable selection and inference procedures for marginal analysis of longitudinal data with missing observations and covariate measurement error
Author(s) -
Yi Grace Y.,
Tan Xianming,
Li Runze
Publication year - 2015
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.1002/cjs.11268
Subject(s) - covariate , missing data , inference , model selection , statistical inference , statistics , computer science , selection (genetic algorithm) , econometrics , marginal distribution , observational error , contrast (vision) , estimating equations , marginal model , data mining , mathematics , regression analysis , machine learning , artificial intelligence , maximum likelihood , random variable
In contrast to extensive attention on model selection for cross‐sectional data, research on model selection for longitudinal data remains largely unexplored. This is particularly the case when data are subject to missingness and measurement error. To address this important problem, we propose marginal methods that simultaneously carry out model selection and estimation for longitudinal data with missing responses and error‐prone covariates. Our methods have several appealing features: the applicability is broad because the methods are developed for a unified framework with marginal generalized linear models; model assumptions are minimal in that no full distribution is required for the response process and the distribution of the true covariates is left unspecified; and the implementation is straightforward. To justify the proposed methods, we provide both theoretical properties and numerical assessments. The Canadian Journal of Statistics 43: 498–518; 2015 © 2015 Statistical Society of Canada