Model Averaging Estimation for Varying-Coefficient Single-Index Models

Abstract

The varying-coefficient single-index model (VCSIM) is widely used in economics, statistics and biology. A model averaging method for VCSIM based on a Mallows-type criterion is proposed to improve prodictive capacity, which allows the number of candidate models to diverge with sample size. Under model misspecification, the asymptotic optimality is derived in the sense of achieving the lowest possible squared errors. The authors compare the proposed model averaging method with several other classical model selection methods by simulations and the corresponding results show that the model averaging estimation has a outstanding performance. The authors also apply the method to a real dataset.