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Maximum likelihood estimation via duality for cell probabilities subject to convex and log-convex constraints

Maximum likelihood estimation via duality for cell probabilities subject to convex and log-convex... This article proposes a method for fitting models subject to a convex and log-convex constraint on the probability vector of a product multinomial (binomial) distribution. We present an iterative algorithm for finding the restricted maximum likelihood estimates (MLEs) of the probability vector and show that the algorithm converges to the true solution. Some examples are discussed to illustrate the method. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Acta Mathematicae Applicatae Sinica Springer Journals

Maximum likelihood estimation via duality for cell probabilities subject to convex and log-convex constraints

Acta Mathematicae Applicatae Sinica , Volume 30 (1) – Apr 26, 2014

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References (16)

Publisher
Springer Journals
Copyright
Copyright © 2014 by Institute of Applied Mathematics, Academy of Mathematics and System Sciences, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelberg
Subject
Mathematics; Applications of Mathematics; Math Applications in Computer Science; Theoretical, Mathematical and Computational Physics
ISSN
0168-9673
eISSN
1618-3932
DOI
10.1007/s10255-014-0275-1
Publisher site
See Article on Publisher Site

Abstract

This article proposes a method for fitting models subject to a convex and log-convex constraint on the probability vector of a product multinomial (binomial) distribution. We present an iterative algorithm for finding the restricted maximum likelihood estimates (MLEs) of the probability vector and show that the algorithm converges to the true solution. Some examples are discussed to illustrate the method.

Journal

Acta Mathematicae Applicatae SinicaSpringer Journals

Published: Apr 26, 2014

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