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Parametric Elliptical Regression Quantiles
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SYSNO ASEP 0493763 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Parametric Elliptical Regression Quantiles Author(s) Hlubinka, D. (CZ)
Šiman, Miroslav (UTIA-B) RID, ORCIDSource Title Revstat Statistical Journal. - : INE - ISSN 1645-6726
Roč. 18, č. 3 (2020), s. 257-280Number of pages 27 s. Publication form Print - P Language eng - English Country PT - Portugal Keywords multiple-output regression ; quantile regression ; nonlinear regression ; elliptical quantile Subject RIV BA - General Mathematics OECD category Pure mathematics R&D Projects GA17-07384S GA ČR - Czech Science Foundation (CSF) GA14-07234S GA ČR - Czech Science Foundation (CSF) Method of publishing Open access Institutional support UTIA-B - RVO:67985556 UT WOS 000557809200002 EID SCOPUS 85090693948 Annotation The article extends linear and nonlinear quantile regression to the case of vector responses by generalizing multivariate elliptical quantiles to a regression context. In particular, it introduces parametric elliptical quantile regression in a general nonlinear multivariate heteroscedastic framework and discusses, investigates, and illustrates the new method in some detail, including basic properties, various parametrizations, possible heteroscedastic patterns, related computational issues, model validation, and a real biometric data example. The method seems suitable for multi-response regression models with symmetric errors, especially if the dimension of responses is less than ten and if the right parametrization of the model follows from the context. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2021 Electronic address https://www.ine.pt/revstat/pdf/ONPARAMETRICELLIPTICALREGRESSIONQUANTILES.pdf
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