Logistic regression is a well known binary classi cation method in the field of sta-tistical learning. Mixed-effect regression models are widely used for the analysis of correlated data such as those found in longitudinal studies. We consider kernel e...
Logistic regression is a well known binary classi cation method in the field of sta-tistical learning. Mixed-effect regression models are widely used for the analysis of correlated data such as those found in longitudinal studies. We consider kernel extensions with semiparametric fixed effects and parametric random effects for the logistic regression. The estimation is performed through the penalized likelihood method based on kernel trick, and our focus is on the efficient computation and the effective hyperpar-ameter selection. For the selection of optimal hyperparameters, cross-validation tech-niques are employed. Numerical results are then presented to indicate the performance of the proposed procedure.