College of Applied Sciences, Beijing University of Technology

In this paper, we focus on the variable
selection for the linear EV 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
procedures.