Maximum Likelihood Estimation for Multinomial-Poisson Models: A Generalization of Birch's Numerical Invariance Results

Authors


Joseph, B. Lang, Department of Statistics and Actuarial Science, University of Iowa, Iowa City, IA, USA. E-mail: joseph-lang@uiowa.edu

Abstract

Abstract.  This study gives a generalization of Birch's log-linear model numerical invariance result. The generalization is given in the form of a sufficient condition for numerical invariance that is simple to verify in practice and is applicable for a much broader class of models than log-linear models. Unlike Birch's log-linear result, the generalization herein does not rely on any relationship between sufficient statistics and maximum likelihood estimates. Indeed the generalization does not rely on the existence of a reduced set of sufficient statistics. Instead, the concept of homogeneity takes centre stage. Several examples illustrate the utility of non-log-linear models, the invariance (and non-invariance) of fitted values, and the invariance (and non-invariance) of certain approximating distributions.

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