Estimation of Single-Index Models Based on Boosting Techniques

摘要

In single-index models the link or response function is not considered as fixed. The data determine the form of the unknown link function. In order to obtain a flexible form of the link function we specify the link function as an expansion in basis function and propose to estimate parameters as well as the link function by weak learners within a boosting framework. It is shown that the method is a strong competitor to existing methods. The method is investigated in simulation studies and applied to real data.