Functional Partial Linear Single-index Model

This paper deals with the problem of predicting the real-valued response variable using explanatory variables containing both multivariate random variable and random curve. The proposed functional partial linear single-index model treats the multivariate random variable as linear part and the random curve as functional single-index part, respectively. To estimate the non-parametric link function, the functional single-index and the parameters in the linear part, a two-stage estimation procedure is proposed. Compared with existing semi-parametric methods, the proposed approach requires no initial estimation and iteration. Asymptotical properties are established for both the parameters in the linear part and the functional single-index. The convergence rate for the non-parametric link function is also given. In addition, asymptotical normality of the error variance is obtained that facilitates the construction of confidence region and hypothesis testing for the unknown parameter. Numerical experiments including simulation studies and a real-data analysis are conducted to evaluate the empirical performance of the proposed method.