General local rank estimation for single-index varying coefficient models

Abstract

In this paper, we consider rank-based estimation of the index coefficient and the functional regression coefficients for the single-index varying coefficient regression model. The consistency and asymptotic normality of the proposed estimators are established under mild assumptions. An extensive Monte-Carlo simulation study demonstrates the robustness and efficiency of the proposed estimators compared to the least squares estimators. An application in ecology is provided and shows that the rank-based regression procedure effectively provides more accurate estimates compared to its least squares counterpart.