Maximum likelihood estimation for constrained parameters of multinomial distributions-Application to Zipf-Mandelbrot models

Authors:
F. Izsák
Affiliations:
ELTE, Institute of Mathematics, P.O. Box 120, 1518 Budapest, Hungary and University of Twente, EWI, P.O. Box 217, 7500 AE Enschede, Netherlands
Venue:
Computational Statistics & Data Analysis
Year:
2006

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Abstract

A numerical maximum likelihood (ML) estimation procedure is developed for the constrained parameters of multinomial distributions. The main difficulty involved in computing the likelihood function is the precise and fast determination of the multinomial coefficients. For this the coefficients are rewritten into a telescopic product. The presented method is applied to the ML estimation of the Zipf-Mandelbrot (ZM) distribution, which provides a true model in many real-life cases. The examples discussed arise from ecological and medical observations. Based on the estimates, the hypothesis that the data is ZM distributed is tested using a chi-square test. The computer code of the presented procedure is available on request by the author.