Open Access
December 2012 Estimation in functional linear quantile regression
Kengo Kato
Ann. Statist. 40(6): 3108-3136 (December 2012). DOI: 10.1214/12-AOS1066


This paper studies estimation in functional linear quantile regression in which the dependent variable is scalar while the covariate is a function, and the conditional quantile for each fixed quantile index is modeled as a linear functional of the covariate. Here we suppose that covariates are discretely observed and sampling points may differ across subjects, where the number of measurements per subject increases as the sample size. Also, we allow the quantile index to vary over a given subset of the open unit interval, so the slope function is a function of two variables: (typically) time and quantile index. Likewise, the conditional quantile function is a function of the quantile index and the covariate. We consider an estimator for the slope function based on the principal component basis. An estimator for the conditional quantile function is obtained by a plug-in method. Since the so-constructed plug-in estimator not necessarily satisfies the monotonicity constraint with respect to the quantile index, we also consider a class of monotonized estimators for the conditional quantile function. We establish rates of convergence for these estimators under suitable norms, showing that these rates are optimal in a minimax sense under some smoothness assumptions on the covariance kernel of the covariate and the slope function. Empirical choice of the cutoff level is studied by using simulations.


Download Citation

Kengo Kato. "Estimation in functional linear quantile regression." Ann. Statist. 40 (6) 3108 - 3136, December 2012.


Published: December 2012
First available in Project Euclid: 22 February 2013

zbMATH: 1296.62104
MathSciNet: MR3097971
Digital Object Identifier: 10.1214/12-AOS1066

Primary: 62G20

Keywords: functional data , nonlinear ill-posed problem , Principal Component Analysis , Quantile regression

Rights: Copyright © 2012 Institute of Mathematical Statistics

Vol.40 • No. 6 • December 2012
Back to Top