A0666
Title: Semiparametric function-on-function quantile regression model with dynamic single-index interactions
Authors: Hanbing Zhu - East China Normal University (China)
Yuanyuan Zhang - Soochow University (China) [presenting]
Yehua Li - University of California at Riverside (United States)
Heng Lian - City university of Hong kong (Hong Kong)
Abstract: A new semiparametric function-on-function quantile regression model with time-dynamic single-index interactions is proposed. Our model is very flexible in taking into account the nonlinear time-dynamic interaction effects of the multivariate longitudinal/functional covariates on the longitudinal response, and most existing quantile regression models for longitudinal data are special cases of our proposed model. We propose to approximate the bivariate nonparametric coefficient functions by tensor product B-splines and employ a check loss minimization approach to estimate the bivariate coefficient functions and the index parameter vector. Under some mild conditions, we establish the asymptotic normality of the estimated single-index coefficients using the projection orthogonalization technique and obtain the convergence rates of the estimated bivariate coefficient functions. Furthermore, we propose a score test to examine whether there exist interaction effects between the covariates. The finite sample performance of the proposed method is illustrated by Monte Carlo simulations and an empirical data analysis.
