Maximum likelihood estimates via duality for log-convex models when cell probabilities are subject to convex constraints
El Barmi, Hammou ; Dykstra, Richard
Ann. Statist., Tome 26 (1998) no. 3, p. 1878-1893 / Harvested from Project Euclid
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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.
Publié le : 1998-10-14
Classification:  Convex constraints,  log-convex constraints,  maximum likelihood,  multinomial,  iterative algorithm,  $ I$-projection,  duality,  convex cones,  62F30,  62G05 
@article{1024691361,
     author = {El Barmi, Hammou and Dykstra, Richard},
     title = {Maximum likelihood estimates via duality for log-convex models
		 when cell probabilities are subject to convex constraints},
     journal = {Ann. Statist.},
     volume = {26},
     number = {3},
     year = {1998},
     pages = { 1878-1893},
     language = {en},
     url = {http://dml.mathdoc.fr/item/1024691361}
}

El Barmi, Hammou; Dykstra, Richard. Maximum likelihood estimates via duality for log-convex models
		 when cell probabilities are subject to convex constraints. Ann. Statist., Tome 26 (1998) no. 3, pp.  1878-1893. http://gdmltest.u-ga.fr/item/1024691361/