Variable selection for semiparametric errors-in-variables regression model with longitudinal data

Abstract

In this paper, we focus on the variable selection for the semiparametric regression model with longitudinal data when some covariates are measured with errors. A new bias-corrected variable selection procedure is proposed based on the combination of the quadratic inference functions and shrinkage estimations. With appropriate selection of the tuning parameters, we establish the consistency and asymptotic normality of the resulting estimators. Extensive Monte Carlo simulation studies are conducted to examine the finite sample performance of the proposed variable selection procedure. We further illustrate the proposed procedure with an application.

Keywords:

• semiparametric errors-in-variables regression model
• variable selection
• longitudinal data
• quadratic inference function

AMS Subject Classification::

• 62G05
• 62G20

Acknowledgements

This work is supported by the National Natural Science Foundation of China (11171012), the Science and Technology project of the faculty adviser of excellent PhD degree thesis of Beijing (20111000503), and the Beijing Municipal Education Commission Foundation (KM201110005029).