. . "2013-06-24+02:00"^^ . "Robust R\u00E9nyi decomposable minimum distance estimators used in decision tree based single top quark signal separation"@en . "\u010Cesk\u00E9 vysok\u00E9 u\u010Den\u00ED technick\u00E9 v Praze" . "RIV/68407700:21340/13:00210457!RIV14-MSM-21340___" . . . "6"^^ . "Robust R\u00E9nyi decomposable minimum distance estimators used in decision tree based single top quark signal separation"@en . "Neb\u0159ich" . "K\u016Fs, V\u00E1clav" . . "Ku\u010Dera, Jan" . "978-80-01-05383-6" . . "Robust R\u00E9nyi decomposable minimum distance estimators used in decision tree based single top quark signal separation" . "Robust R\u00E9nyi decomposable minimum distance estimators used in decision tree based single top quark signal separation" . . "Minimum distance estimators; robustness; phi-divergences; decision trees"@en . . . "103172" . . . . "[211E6A5D685B]" . . . . "21340" . . "2"^^ . . "SPMS 2013 Stochastic and Physical Monitoring Systems Proceedings" . "RIV/68407700:21340/13:00210457" . "2"^^ . . . . "R\u00E9nyi decomposable information divergences are the very new and promising concept used in statistical inference. They bring high robustness properties and relative practical feasibility. M-C simulation results for the Minimum R\u00E9nyi Distance (MReD) estimates in the case of very sparse and scattered data (data with high variance) or small sample data sets are presented and the effect of input parameter alpha to the robustness is shown. Heuristic approach is proposed for such MReD estimates when the strict minimization leads to delta functions. Subsequent R\u00E9nyi Divergence based Decision Tree signal separations are going to be carried out for the Single Top Quark decay channel, i.e. the data samples coming from high energy particle accelerator Tevatron in Fermilab obtained at the energy level 1.96TeV and the beam luminosity 9.5fb-1."@en . . "P(LG12020), Z(MSM6840770039)" . "Praha" . "R\u00E9nyi decomposable information divergences are the very new and promising concept used in statistical inference. They bring high robustness properties and relative practical feasibility. M-C simulation results for the Minimum R\u00E9nyi Distance (MReD) estimates in the case of very sparse and scattered data (data with high variance) or small sample data sets are presented and the effect of input parameter alpha to the robustness is shown. Heuristic approach is proposed for such MReD estimates when the strict minimization leads to delta functions. Subsequent R\u00E9nyi Divergence based Decision Tree signal separations are going to be carried out for the Single Top Quark decay channel, i.e. the data samples coming from high energy particle accelerator Tevatron in Fermilab obtained at the energy level 1.96TeV and the beam luminosity 9.5fb-1." .