Double penalized variable selection procedure for partially linear models with longitudinal data

Based on the double penalized estimation method, a new variable selection procedure is proposed for partially linear models with longitudinal data. The proposed procedure can avoid the effects of the nonparametric estimator on the variable selection for the parameters components. Under some regularity conditions, the rate of convergence and asymptotic normality of the resulting estimators are established. In addition, to improve efficiency for regression coefficients, the estimation of the working covariance matrix is involved in the proposed iterative algorithm. Some simulation studies are carried out to demonstrate that the proposed method performs well.