Varying coefficient modeling via least squares support vector regression
The varying coefficient regression model has received a great deal of attention as an important tool for modeling the dynamic changes of regression coefficients in the social and natural sciences. Lots of efforts have been devoted to develop effective estimation methods for such regression model. In this paper we propose a method for fitting the varying coefficient regression model using the least squares support vector regression technique, which analyzes the dynamic relation between a response and a group of covariates. We also consider a generalized cross validation method for choosing the hyperparameters which affect the performance of the proposed method. We provide a method for estimating the confidence intervals of coefficient functions. The proposed method is evaluated through simulation and real example studies.
varying coefficient model least squares support vector regression generalized cross validation confidence interval model selection