 Simultaneous variable selection and estimation for multivariate multilevel longitudinal data with both continuous and binary responsesHaocheng Li, Di Shu, Yukun Zhang and Grace Y. Yi Computational Statistics & Data Analysis, 2018, vol. 118, issue C, 126-137 Abstract: Complex structured data settings are studied where outcomes are multivariate and multilevel and are collected longitudinally. Multivariate outcomes include both continuous and discrete responses. In addition, the data contain a large number of covariates but only some of them are important in explaining the dynamic features of the responses. To delineate the complex association structures of the responses, a model with correlated random effects is proposed. To handle the large dimensionality of covariates, a simultaneous variable selection and parameter estimation method is developed. To implement the method, a computationally feasible algorithm is described. The proposed method is evaluated empirically by simulation studies and illustrated by analyzing the data arising from the Waterloo Smoking Prevention Project. Keywords: Longitudinal data; Mixed effects model; Multivariate multilevel longitudinal data; Penalized quasi-likelihood; Variable selection (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:118:y:2018:i:c:p:126-137 DOI: 10.1016/j.csda.2017.09.004 Access Statistics for this article Computational Statistics & Data Analysis is currently edited by S.P. Azen More articles in Computational Statistics & Data Analysis from Elsevier |
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