Variable selection for semiparametric random-effects conditional density models with longitudinal data

Abstract

Variable selection using regularization approaches is an essential part of any statistical analysis and yet has been somewhat neglected for the semiparametric random-effects conditional density (RECD) models with longitudinal data. In this paper, we show how the regularization approach for variable selection can be adapted to the RECD models with longitudinal data. The computational and theoretical properties for variable selection consistency are established. Comprehensive simulation studies and a real data analysis further demonstrate the merits of our approach.

Keywords:

• Longitudinal data
• random-effects conditional density models
• regularization
• selection consistency
• variable selection

Acknowledgements

The authors thank three anonymous referees and the associate editor for their careful reading of the paper and insightful comments. Xiaohui Yuan was partly supported by the NSFC (No. 11571051, 11671054, 11701043).

Additional information

Funding

The authors thank three anonymous referees and the associate editor for their careful reading of the paper and insightful comments. Xiaohui Yuan was partly supported by the NSFC (No. 11571051, 11671054, 11701043)