Testing linearity in partial functional linear quantile regression model based on regression rank scores

Abstract

This paper investigates the hypothesis test of the parametric component in partial functional linear quantile regression model in which the dependent variable is related to both a vector of finite length and a function-valued random variable as predictor variables. A quantile rank score test based on functional principal component analysis is developed. Under mild conditions, we establish the consistency of the proposed test statistic, and show that the proposed test can detect Pitman local alternatives converging to the null hypothesis at the usual parametric rate. A simulation study shows that the proposed test procedure has good size and power with finite sample sizes. Finally, an illustrative example is given through fitting the Berkeley growth data and testing the effect of gender on the height of kids.