Variable selection and estimation for longitudinal survey data

Li Wang, Suojin Wang, Guannan Wang

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


There is wide interest in studying longitudinal surveys where sample subjects are observed successively over time. Longitudinal surveys have been used in many areas today, for example, in the health and social sciences, to explore relationships or to identify significant variables in regression settings. This paper develops a general strategy for the model selection problem in longitudinal sample surveys. A survey weighted penalized estimating equation approach is proposed to select significant variables and estimate the coefficients simultaneously. The proposed estimators are design consistent and perform as well as the oracle procedure when the correct submodel was known. The estimating function bootstrap is applied to obtain the standard errors of the estimated parameters with good accuracy. A fast and efficient variable selection algorithm is developed to identify significant variables for complex longitudinal survey data. Simulated examples are illustrated to show the usefulness of the proposed methodology under various model settings and sampling designs. © 2014 Elsevier Inc.
Original languageEnglish (US)
Pages (from-to)409-424
Number of pages16
JournalJournal of Multivariate Analysis
StatePublished - Sep 2014
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUS-CI-016-04
Acknowledgements: The research of L. Wang was partially supported by NSF grants DMS-0905730, DMS-1106816, DMS-1309800 and the ASA/NSF/BLS research fellow program. The research of S. Wang was partially supported by Award Number KUS-CI-016-04, made by King Abdullah University of Science and Technology (KAUST). The views expressed in this paper are those of the authors and do not necessarily reflect the policies of the US Bureau of Labor Statistics.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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