Nonparametric Multiple-Output Center-Outward Quantile Regression

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F25%3A00602391" target="_blank" >RIV/67985556:_____/25:00602391 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.tandfonline.com/doi/full/10.1080/01621459.2024.2366029" target="_blank" >https://www.tandfonline.com/doi/full/10.1080/01621459.2024.2366029</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/01621459.2024.2366029" target="_blank" >10.1080/01621459.2024.2366029</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Nonparametric Multiple-Output Center-Outward Quantile Regression

Popis výsledku v původním jazyce

Building on recent measure-transportation-based concepts of multivariate quantiles, we are considering the problem of nonparametric multiple-output quantile regression. Our approach defines nested conditional center-outward quantile regression contours and regions with given conditional probability content, the graphs of which constitute nested center-outward quantile regression tubes with given unconditional probability content. These (conditional and unconditional) probability contents do not depend on the underlying distribution—an essential property of quantile concepts. Empirical counterparts of these concepts are constructed, yielding interpretable empirical contours, regions, and tubes which are shown to consistently reconstruct (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. Its potential as a data-analytic tool is illustrated on some real datasets. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Název v anglickém jazyce

Nonparametric Multiple-Output Center-Outward Quantile Regression

Popis výsledku anglicky

Building on recent measure-transportation-based concepts of multivariate quantiles, we are considering the problem of nonparametric multiple-output quantile regression. Our approach defines nested conditional center-outward quantile regression contours and regions with given conditional probability content, the graphs of which constitute nested center-outward quantile regression tubes with given unconditional probability content. These (conditional and unconditional) probability contents do not depend on the underlying distribution—an essential property of quantile concepts. Empirical counterparts of these concepts are constructed, yielding interpretable empirical contours, regions, and tubes which are shown to consistently reconstruct (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. Its potential as a data-analytic tool is illustrated on some real datasets. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of the American Statistical Association

ISSN

0162-1459

e-ISSN

1537-274X

Svazek periodika

120

Číslo periodika v rámci svazku

550

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

15

Strana od-do

818-832

Kód UT WoS článku

001317613700001

EID výsledku v databázi Scopus

2-s2.0-85197505848