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October 1998 Maximum likelihood estimates via duality for log-convex models when cell probabilities are subject to convex constraints
Richard Dykstra, Hammou El Barmi
Ann. Statist. 26(5): 1878-1893 (October 1998). DOI: 10.1214/aos/1024691361

Abstract

The purpose of this article is to derive and illustrate a method for fitting models involving both convex and log-convex constraints on the probability vector(s) of a (product) multinomial distribution. We give a two-step algorithm to obtain maximum likelihood estimates of the probability vector(s) and show that it is guaranteed to converge to the true solution. Some examples are discussed which illustrate the procedure.

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Richard Dykstra. Hammou El Barmi. "Maximum likelihood estimates via duality for log-convex models when cell probabilities are subject to convex constraints." Ann. Statist. 26 (5) 1878 - 1893, October 1998. https://doi.org/10.1214/aos/1024691361

Information

Published: October 1998
First available in Project Euclid: 21 June 2002

zbMATH: 0929.62029
MathSciNet: MR1673282
Digital Object Identifier: 10.1214/aos/1024691361

Subjects:
Primary: 62F30
Secondary: 62G05

Rights: Copyright © 1998 Institute of Mathematical Statistics

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Vol.26 • No. 5 • October 1998
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