The Annals of Statistics
- Ann. Statist.
- Volume 40, Number 6 (2012), 3108-3136.
Estimation in functional linear quantile regression
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.
Ann. Statist., Volume 40, Number 6 (2012), 3108-3136.
First available in Project Euclid: 22 February 2013
Permanent link to this document
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Mathematical Reviews number (MathSciNet)
Zentralblatt MATH identifier
Primary: 62G20: Asymptotic properties
Kato, Kengo. Estimation in functional linear quantile regression. Ann. Statist. 40 (2012), no. 6, 3108--3136. doi:10.1214/12-AOS1066. https://projecteuclid.org/euclid.aos/1361542076
- Supplementary material: Supplement to “Estimation in functional linear quantile regression”. This supplementary file contains the additional discussion on the connection to nonlinear ill-posed inverse problems, technical proofs omitted in the main body, some useful technical tools and additional simulation results.