0434510 20240103204954.020160303235959.9 000354714100004 84908291355 10.1007/s11749-014-0405-3 16 s. P cav_un_epca*02552111133-0686242 (2015)249-264 multivariate quantile elliptical quantile quantile regression multivariate statistical inference portfolio optimization cav_un_auth*0213091 Hlubinka D. CZ cav_un_auth*0266474 Šiman Miroslav Stochastická informatika Department of Stochastic Informatics SI SI UTIA-B Department of Stochastic Informatics Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2014/SI/siman-0434510.pdf GA14-07234S GA ČR cav_un_auth*0307008 Inspired by nonlinear quantile regression, the article introduces, investigates, discusses, and illustrates a new concept of generalized elliptical location quantiles. They may require less stringent moment assumptions, be less sensitive to outliers, be less rigid, employ more a priori information regarding the location of the distribution, and have higher potential for various regression generalizations than their common elliptical predecessor defined in the convex optimization framework by means of standard linear quantile regression. Furthermore, they still include an equivalent of their predecessor as a special case and inherit most of its favorable features such as the probability interpretation, natural equivariance properties, and good behavior for elliptical and symmetric distributions, which is demonstrated both by theoretical results and data examples with convincing graphical output. 2016 BA 4 A 4a 20231122140556.9 RVO:67985556 http://hdl.handle.net/11104/0239351 cav_un_auth*0296001 MFF UK Univerzita Karlova v Praze, Matematicko-fyzikální fakulta CZ S STATISTICS&PROBABILITY 1.486 0.243 7.9 0.00335 1.582 564 1.537 Q1 1.086 37 Q2 77.983 62.2 Q1 Q1 62.195 2015 Soubory v repozitáři: siman-0434510.pdf 84908291355 SCOPUS 000354714100004 WOS cav_un_epca*0255211 Test 1133-0686 1863-8260 Roč. 24 č. 2 2015 249 264