Estimation in functional linear quantile regression

TL;DR

This paper develops estimators for functional linear quantile regression with discretely observed covariates, allowing the quantile index to vary, and establishes their optimal convergence rates.

Contribution

It introduces a novel estimation approach for functional linear quantile regression with varying quantile index and discretely observed data, including monotonicity correction.

Findings

Proposed estimators achieve minimax optimal convergence rates.

Simulation studies demonstrate the effectiveness of the estimators.

Monotonized estimators satisfy the quantile monotonicity constraint.

Abstract

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.