Nonparametric Multiple-Output Center-Outward Quantile Regression
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Nonparametric Multiple-Output Center-Outward Quantile Regression
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10103 - Statistics and probability
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Journal of the American Statistical Association
ISSN
0162-1459
e-ISSN
1537-274X
Volume of the periodical
120
Issue of the periodical within the volume
550
Country of publishing house
US - UNITED STATES
Number of pages
15
Pages from-to
818-832
UT code for WoS article
001317613700001
EID of the result in the Scopus database
2-s2.0-85197505848