Minimum divergence estimators based on grouped data

0410647 20240103182229.020060210235959.9 Minimum divergence estimators based on grouped data 12 s. cav_un_epca*02507910020-3157Annals of the Institute of Statistical Mathematics532 (2001)277-288 minimum divergence estimators random quantization asymptotic normality cav_un_auth*0212088 Menéndez M. L. ES cav_un_auth*0015540 Morales D. ES cav_un_auth*0021073 Pardo L. ES cav_un_auth*0101218 Vajda Igor UTIA-B Ústav teorie informace a automatizace AV ČR, v. v. i. 12B GA102/99/1137 GA ČR cav_un_auth*0004432 AV0Z1075907 It is shown that an optimal grouping of data (quantification) leads to a negligible inefficiency in continuous parametric models. Robust estimators achieving the negligible inefficiency are introduced and their asymptotic theory is established. BB SI http://hdl.handle.net/11104/0130735 UTIA-B 20010116 2001 cav_un_epca*0250791 Annals of the Institute of Statistical Mathematics 0020-3157 1572-9052 Roč. 53 č. 2 2001 277 288