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B0451
Title: Functional single index quantile regression models Authors:  Peijun Sang - University of Waterloo (Canada) [presenting]
Jiguo Cao - Simon Fraser University (Canada)
Abstract: It is known that functional single index regression models can achieve better prediction accuracy than functional linear models or fully nonparametric models, when the target is to predict a scalar response using a function-valued covariate. However, the performance of these models may be adversely affected by extremely large values or skewness in the response. In addition, they are not able to offer a full picture of the conditional distribution of the response. Motivated by using trajectories of PM10 concentrations of last day to predict the maximum PM10 concentration of the current day, a functional single-index quantile regression model is proposed to address those issues. A generalized profiling method is employed to estimate the model. Simulation studies are conducted to investigate the finite sample performance of the proposed estimator. We apply the proposed framework to predict the maximal value of PM10 concentrations based on the intraday PM10 concentrations of the previous day.