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                                       Details van artikel 14 van 19 gevonden artikelen
 
 
  Robust minimum distance inference based on combined distances
 
 
Titel: Robust minimum distance inference based on combined distances
Auteur: Park, Chanseok
Basu, Ayanendranath
Basu, Srabashi
Verschenen in: Communications in statistics
Paginering: Jaargang 24 (1995) nr. 3 pagina's 653-673
Jaar: 1995
Inhoud: The minimum disparity estimators proposed by Lindsay (1994) for discrete models form an attractive subclass of minimum distance estimators which achieve their robustness without sacrificing first order efficiency at the model. Similarly, disparity test statistics are useful robust alternatives to the likelihood ratio test for testing of hypotheses in parametric models; they are asymptotically equivalent to the likelihood ratio test statistics under the null hypothesis and contiguous alternatives. Despite their asymptotic optimality properties, the small sample performance of many of the minimum disparity estimators and disparity tests can be considerably worse compared to the maximum likelihood estimator and the likelihood ratio test respectively. In this paper we focus on the class of blended weight Hellinger distances, a general subfamily of disparities, and study the effects of combining two different distances within this class to generate the family of “combined” blended weight Hellinger distances, and identify the members of this family which generally perform well. More generally, we investigate the class of "combined and penal-ized" blended weight Hellinger distances; the penalty is based on reweighting the empty cells, following Harris and Basu (1994). It is shown that some members of the combined and penalized family have rather attractive properties
Uitgever: Taylor & Francis
Bronbestand: Elektronische Wetenschappelijke Tijdschriften
 
 

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