0436865 20240103205248.220141204235959.9 000304722200008 10.1016/j.jspi.2012.03.019 28 s. P cav_un_epca*02571160378-37581429 (2012)2574-2585Elsevier Parametric model Pseudodistance Influence function cav_un_auth*0255501 Broniatowski M. FR cav_un_auth*0101218 Vajda Igor Stochastická informatika Department of Stochastic Informatics SI SI UTIA-B Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2012/SI/vajda-0436865.pdf CEZ:AV0Z10750506 The aim of this paper is to introduce new statistical criteria for estimation, suitable for inference in models with common continuous support. This proposal is in the direct line of a renewed interest for divergence based inference tools imbedding the most classical ones, such as maximum likelihood, Chi-square or Kullback-Leibler. General pseudodistances with decomposable structure are considered, they allowing defining minimum pseudodistance estimators, without using nonparametric density estimators. A special class of pseudodistances indexed by alpha > 0, leading for alpha down arrow 0 to the Kullback-Leibler divergence, is presented in detail. Corresponding estimation criteria are developed and asymptotic properties are studied. The estimation method is then extended to regression models. Finally, some examples based on Monte Carlo simulations are discussed. 2015 BB RVO:67985556 http://hdl.handle.net/11104/0240501 S STATISTICSPROBABILITY 0.784 0.084 7.2 0.01798 0.617 3205 285 39 0.913 19.93 0.82 Q2 34.702 38.034 Q3 Q2 2012 000304722200008 WOS cav_un_epca*0257116 Journal of Statistical Planning and Inference 0378-3758 1873-1171 Roč. 142 č. 9 2012 2574 2585 Elsevier