A More Accurate Estimation of Semiparametric Logistic Regression

Growing interest in genomics research has called for new semiparametric models based on kernel machine regression for modeling health outcomes. Models containing redundant predictors often show unsatisfactory prediction performance. Thus, our task is to construct a method which can guarantee the estimation accuracy by removing redundant variables. Specifically, in this paper, based on the regularization method and an innovative class of garrotized kernel functions, we propose a novel penalized kernel machine method for a semiparametric logistic model. Our method can promise us high prediction accuracies, due to its capability of flexibly describing the complicated relationship between responses and predictors and its compatibility of the interactions among the predictors. In addition, our method can also remove the redundant variables. Our numerical experiments demonstrate that our method yields higher prediction accuracies compared to competing approaches.