Variable Selection in Joint Mean and Covariance Models

Chaofeng Kou () and Jianxin Pan ()
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Chaofeng Kou: University of Manchester, Department of Mathematics
Jianxin Pan: University of Manchester, Department of Mathematics

Chapter Chapter 13 in Recent Developments in Multivariate and Random Matrix Analysis, 2020, pp 219-244 from Springer

Abstract: Abstract In this paper, we propose a penalized maximum likelihood method for variable selection in joint mean and covariance models for longitudinal data. Under certain regularity conditions, we establish the consistency and asymptotic normality of the penalized maximum likelihood estimators of parameters in the models. We further show that the proposed estimation method can correctly identify the true models, as if the true models would be known in advance. We also carry out real data analysis and simulation studies to assess the small sample performance of the new procedure, showing that the proposed variable selection method works satisfactorily.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-56773-6_13

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DOI: 10.1007/978-3-030-56773-6_13

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