0425059 20240103203908.520140226235959.9 84891609541 000328930900011 10.1080/03610926.2012.661509 11 s. P cav_un_epca*02525200361-0926432 (2014)377-387Taylor & Francis data depth multivariate quantile process capability index precision index regression quantile cav_un_auth*0266474 Stochastická informatika Department of Stochastic Informatics SI SI Department of Stochastic Informatics Šiman Miroslav UTIA-B Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2014/SI/siman-0425059.pdf cav_un_auth*0217941 1M06047 GA MŠk CZ General multivariate quantiles are employed to extend the classic univariate process precision index to the multivariate context under very mild conditions. Using halfspace depth regions for this purpose is especially recommended because it leads to both computational simplicity and natural generalizations to the tool-wear setup thanks to some recent advances in multiple-output and projectional quantile regression. A few examples are included to illustrate how the methodology might work in practice. BA 2014 RVO:67985556 http://hdl.handle.net/11104/0231503 S STATISTICSPROBABILITY 0.234 0.435 Q3 5.368 2.049 Q4 Q4 2014 84891609541 SCOPUS 000328930900011 WOS cav_un_epca*0252520 Communications in Statistics - Theory and Methods 0361-0926 1532-415X Roč. 43 č. 2 2014 377 387 Taylor & Francis