0491756 20240103220256.420180726235959.9 1 s. cav_un_epca*0491757122-122Leuven2018 Robust estimation Multivariate data Location and scatter Shape estimator Symmetry test cav_un_auth*0263018 Kalina Jan Oddělení strojového učení Department of Machine Learning UIVT-O Department of Machine Learning Ústav informatiky AV ČR, v. v. i. cav_un_auth*0266474 Šiman Miroslav Stochastická informatika Department of Stochastic Informatics SI SI UTIA-B Department of Stochastic Informatics CZ Ústav teorie informace a automatizace AV ČR, v. v. i. https://wis.kuleuven.be/events/icors18/BookOfAbstracts The basis for our work is a theoretical result explaining how various multivariate location and scatter estimators capture the symmetry of the underlying distribution. Various forms of symmetry considered in the paper include central symmetry, marginal symmetry, symmetry around an affine subspace, and symmetry around a coordinate axis. Very general sufficient conditions are formulated, which ensure various symmetry properties of functionals corresponding to location or scatter. There is a variety of robust multivariate estimators, which fulfil these sufficient conditions. cav_un_auth*0362669 ICORS 2018. International Conference on Robust Statistics 20180702 20180706 Leuven BE BA 10000 10100 10103 2019 4 O 4o 20231122143313.9 RVO:67985807 RVO:67985556 http://hdl.handle.net/11104/0285396 S 2018 Soubory v repozitáři: a0491756.pdf SCOPUS PUBMED WOS cav_un_epca*0491757 ICORS 2018. Book of Abstracts 122 122 Leuven 2018