Robust variable selection in semiparametric mean-covariance regression for longitudinal data analysis

This paper considers robust semiparametric smooth-threshold generalized estimating equations for the analysis of longitudinal data based on the modified Cholesky decomposition and B-spline approximations. The proposed method can automatically eliminate inactive predictors by setting the corresponding parameters to be zero, and simultaneously estimate the mean regression coefficients, generalized autoregressive coefficients and innovation variances. In order to overcome the outliers in either the response or/and the covariate domain, we use a bounded score function and leverage-based weights to achieve better robustness. Moreover, the proposed estimators have desired large sample properties including consistency and oracle property. Finally, Monte Carlo simulation studies are conducted to investigate the robustness and efficiency of the proposed method under different contaminations.