Functional single-index composite quantile regression

The functional single-index model is a very flexible semiparametric model when modeling the relationship between a scalar response and functional predictors. However, the efficiency of the model may be affected by non-normal errors. So, in this paper, we propose functional single index composite quantile regression. The unknown slope function and link function are estimated by using B-spline basis functions. The convergence rates of the estimators are established. Some simulation studies and an application of NIR spectroscopy dataset are presented to illustrate the performance of the proposed methodologies.