Variable Selection in Joint Mean and Covariance Models

Chaofeng Kou, Jianxin Pan

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


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.
Original languageEnglish
Title of host publicationRecent Developments in Multivariate and Random Matrix Analysis
Subtitle of host publicationFestschrift in Honour of Dietrich von Rosen
EditorsThomas Holgersson, Martin Singull
PublisherSpringer Nature
ISBN (Electronic)978-3-030-56773-6
ISBN (Print)978-3-030-56772-9
Publication statusE-pub ahead of print - 18 Sept 2020


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