Simultaneous variable selection and estimation of multivariate panel count data

Lei Ge

doi:10.1016/j.jmva.2025.105559

Simultaneous variable selection and estimation of multivariate panel count data

2025-12-01

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DOI：10.1016/j.jmva.2025.105559

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Abstract

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Panel count data are a general type of data arising from the studies on recurrent events and occur when the observed information on each study subject consists of only the numbers of the occurrences of the recurrent events between successive examinations. It is easy to see that such data can occur in many fields, including economic studies, medical studies and social sciences. This paper considers regression analysis of multivariate panel count data with the focus on variable selection and estimation of significant covariate effects. For the problem, a minimum information criterion-based method is proposed and an expectation-maximization algorithm is developed for the determination of the proposed estimator. Furthermore, the resulting estimator is shown to have the desirable oracle property and a simulation study is performed and confirms the good finite-sample properties of the proposed method. Finally the method is applied to a set of real data arising from a skin cancer study.

Keywords:

EM algorithm

Minimum information criterion

Multivariate panel count datax

Proportional mean model

Variable selection

Simultaneous variable selection and estimation of multivariate panel count data

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