RT info:eu-repo/semantics/article
T1 Nonparametric multiple-output center-outward quantile regression
A1 del Barrio, Eustasio
A1 Sanz, Alberto González
A1 Hallin, Marc
K1 Estadística
K1 Center-outward quantiles; Multiple-output regression; Optimal transport
AB Building on recent measure-transportation-based concepts of multivariate quantiles, we are considering theproblem of nonparametric multiple-output quantile regression. Our approach defines nested conditionalcenter-outward quantile regression contours and regions with given conditional probability content, thegraphs of which constitute nested center-outward quantile regression tubes with given unconditional prob-ability content; these (conditional and unconditional) probability contents do not depend on the underlyingdistribution—an essential property of quantile concepts. Empirical counterparts of these concepts areconstructed, yielding interpretable empirical contours, regions, and tubes which are shown to consistentlyreconstruct (in the Pompeiu-Hausdorff topology) their population versions. Our method is entirely non-parametric and performs well in simulations—with possible heteroscedasticity and nonlinear trends. Itspotential as a data-analytic tool is illustrated on some real datasets. Supplementary materials for this articleare available online, including a standardized description of the materials available for reproducing the work.
PB TAYLOR & FRANCIS INC
SN 0162-1459
YR 2025
FD 2025
LK https://uvadoc.uva.es/handle/10324/82067
UL https://uvadoc.uva.es/handle/10324/82067
LA spa
NO JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 2025, VOL. 120, NO. 550, 818–832: Theory and Methods
NO Producción Científica
DS UVaDOC
RD 23-ene-2026