0493763 20240903121019.520180926235959.9 85090693948 000557809200002 27 s. P cav_un_epca*03625391645-6726183 (2020)257-280INE multiple-output regression quantile regression nonlinear regression elliptical quantile cav_un_auth*0213091 Hlubinka D. CZ cav_un_auth*0266474 Šiman Miroslav UTIA-B Stochastická informatika Department of Stochastic Informatics SI SI Department of Stochastic Informatics CZ Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2018/SI/siman-0493763.pdf https://www.ine.pt/revstat/pdf/ONPARAMETRICELLIPTICALREGRESSIONQUANTILES.pdf GA17-07384S GA ČR cav_un_auth*0345381 GA14-07234S GA ČR cav_un_auth*0307008 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. WOS BA 10000 10100 10101 2021 RVO:67985556 http://hdl.handle.net/11104/0289349 cav_un_auth*0296001 Univerzita Karlova v Praze, Matematicko-fyzikální fakulta MFF UK CZ S 3+4 Article Statistics Probability A STATISTICS&PROBABILITY 1.183 0.3 7.8 0.00049 0.403 385 22 0.538 26.14 1 91 Q3 1.231 40 Q3 16.716 38.8 Q4 Q3 0.37 Q3 38.8 2020 85090693948 SCOPUS PUBMED 000557809200002 WOS cav_un_epca*0362539 Revstat Statistical Journal 1645-6726 2183-0371 Roč. 18 č. 3 2020 257 280 INE