We consider a prediction of a scalar variable based on both a function-valued variable and a finite number of real-valued variables.

We consider two different approaches, which are shown to achieve the same convergence rate of the mean squared prediction error under respective assumptions.

Jun 24, 2011 · We consider a prediction of a scalar variable based on both a function-valued variable and a finite number of real-valued variables.

This paper studies estimation in partial functional linear quantile regression in which the dependent variable is related to both a vector of finite length ...

Jan 1, 2012 · We consider a prediction of a scalar variable based on both a function-valued variable and a finite number of real-valued variables.

TL;DR: In this article, a general formulation is used to treat mean regression, median regression, quantile regression and robust mean regression in one setting ...

The optimal convergence rate depends on a delicate balance among the smoothness of b, the smoothness of x, and the smoothness of the autoco- variance of the ...

practical interest in the slope centers on its application for the purpose of prediction, rather than on its significance in its own right. We show that the.

Generalized partially... · Asymptotic inference · Application

In this paper, a generalized partially functional linear regression model is proposed and the asymptotic property of the proposed estimated coefficients in ...