Simultaneous variable selection and estimation in semiparametric regression of mixed panel count data.

TitleSimultaneous variable selection and estimation in semiparametric regression of mixed panel count data.
Publication TypeJournal Article
Year of Publication2024
AuthorsGe, L, Hu, T, Li, Y
JournalBiometrics
Volume80
Issue1
Paginationujad041
ISSN Number1541-0420
KeywordsAlgorithms, Computer Simulation, Likelihood Functions
Abstract

Mixed panel count data represent a common complex data structure in longitudinal survey studies. A major challenge in analyzing such data is variable selection and estimation while efficiently incorporating both the panel count and panel binary data components. Analyses in the medical literature have often ignored the panel binary component and treated it as missing with the unknown panel counts, while obviously such a simplification does not effectively utilize the original data information. In this research, we put forward a penalized likelihood variable selection and estimation procedure under the proportional mean model. A computationally efficient EM algorithm is developed that ensures sparse estimation for variable selection, and the resulting estimator is shown to have the desirable oracle property. Simulation studies assessed and confirmed the good finite-sample properties of the proposed method, and the method is applied to analyze a motivating dataset from the Health and Retirement Study.

DOI10.1093/biomtc/ujad041
Citation Key13808
PubMed ID38465988
Grant List12171328 / / National Natural Science Foundation of China /
Z210003 / / Beijing Municipal Natural Science Foundation /