Reference : Maximum likelihood characterization of distributions
Scientific journals : Article
Physical, chemical, mathematical & earth Sciences : Mathematics
http://hdl.handle.net/10993/26296
Maximum likelihood characterization of distributions
English
Duerinckx, Mitia [> >]
Ley, Christophe []
Swan, Yvik mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Mathematics Research Unit >]
2014
Bernoulli
Chapman & Hall
20
2
775-802
Yes
International
1350-7265
London
UK
[en] Location parameter ; Maximum Likelihood Estimator ; Minimal necessary sample size ; One-parameter group family ; Scale parameter ; Score function
[en] A famous characterization theorem due to C. F. Gauss states that the maximum likelihood estimator (MLE) of the parameter in a location family is the sample mean for all samples of all sample sizes if and only if the family is Gaussian. There exist many extensions of this result in diverse directions, most of them focussing on location and scale families. In this paper we propose a unified treatment of this literature by providing general MLE characterization theorems for one-parameter group families (with particular attention on location and scale parameters). In doing so we provide tools for determining whether or not a given such family is MLE-characterizable, and, in case it is, we define the fundamental concept of minimal necessary sample size at which a given characterization holds. Many of the cornerstone references on this topic are retrieved and discussed in the light of our findings, and several new characterization theorems are provided. Of particular interest is that one part of our work, namely the introduction of so-called equivalence classes for MLE characterizations, is a modernized version of Daniel Bernoulli's viewpoint on maximum likelihood estimation.
http://hdl.handle.net/10993/26296

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