Directional quantile regression in R

Pavel Boček; Miroslav Šiman

Kybernetika (2017)

  • Volume: 53, Issue: 3, page 480-492
  • ISSN: 0023-5954

Abstract

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Recently, the eminently popular standard quantile regression has been generalized to the multiple-output regression setup by means of directional regression quantiles in two rather interrelated ways. Unfortunately, they lead to complicated optimization problems involving parametric programming, and this may be the main obstacle standing in the way of their wide dissemination. The presented R package modQR is intended to address this issue. It originates as a quite faithful translation of the authors' moQuantile toolbox for Octave and MATLAB, and provides all the necessary computational support for both the directional multiple-output quantile regression methods to the wide statistical public. The article offers a concise summary of the statistical theory behind modQR, overviews the package in brief, points out its departures from moQuantile, comments on its use and performance, and demonstrates its application.

How to cite

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Boček, Pavel, and Šiman, Miroslav. "Directional quantile regression in R." Kybernetika 53.3 (2017): 480-492. <http://eudml.org/doc/294839>.

@article{Boček2017,
abstract = {Recently, the eminently popular standard quantile regression has been generalized to the multiple-output regression setup by means of directional regression quantiles in two rather interrelated ways. Unfortunately, they lead to complicated optimization problems involving parametric programming, and this may be the main obstacle standing in the way of their wide dissemination. The presented R package modQR is intended to address this issue. It originates as a quite faithful translation of the authors' moQuantile toolbox for Octave and MATLAB, and provides all the necessary computational support for both the directional multiple-output quantile regression methods to the wide statistical public. The article offers a concise summary of the statistical theory behind modQR, overviews the package in brief, points out its departures from moQuantile, comments on its use and performance, and demonstrates its application.},
author = {Boček, Pavel, Šiman, Miroslav},
journal = {Kybernetika},
keywords = {multivariate quantile; regression quantile; halfspace depth; regression depth; depth contour},
language = {eng},
number = {3},
pages = {480-492},
publisher = {Institute of Information Theory and Automation AS CR},
title = {Directional quantile regression in R},
url = {http://eudml.org/doc/294839},
volume = {53},
year = {2017},
}

TY - JOUR
AU - Boček, Pavel
AU - Šiman, Miroslav
TI - Directional quantile regression in R
JO - Kybernetika
PY - 2017
PB - Institute of Information Theory and Automation AS CR
VL - 53
IS - 3
SP - 480
EP - 492
AB - Recently, the eminently popular standard quantile regression has been generalized to the multiple-output regression setup by means of directional regression quantiles in two rather interrelated ways. Unfortunately, they lead to complicated optimization problems involving parametric programming, and this may be the main obstacle standing in the way of their wide dissemination. The presented R package modQR is intended to address this issue. It originates as a quite faithful translation of the authors' moQuantile toolbox for Octave and MATLAB, and provides all the necessary computational support for both the directional multiple-output quantile regression methods to the wide statistical public. The article offers a concise summary of the statistical theory behind modQR, overviews the package in brief, points out its departures from moQuantile, comments on its use and performance, and demonstrates its application.
LA - eng
KW - multivariate quantile; regression quantile; halfspace depth; regression depth; depth contour
UR - http://eudml.org/doc/294839
ER -

References

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