Variable selection for generalized varying coefficient models with longitudinal data

In this paper, we apply the penalized quadratic inference function to perform variable selection and estimation simultaneously for generalized varying coefficient models with longitudinal data. The proposed approach is based on basis function approximations and the group SCAD penalty, which can incorporate information on the correlation structure within the same subject to achieve an efficient estimator. Furthermore, we discuss the asymptotic theory of our proposed procedure under suitable conditions, including consistency in variable selection and the oracle property in estimation. Finally, monte carlo simulations and a real data analysis are conducted to examine the finite sample performance of the proposed procedure.