Maximum likelihood estimates via duality for log-convex models when cell probabilities are subject to convex constraints

• 成果类型: Article
• 署名作者: El Barmi, H; Dykstra, R
• 署名单位: Kansas State University; University of Iowa
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• 发表日期: 1998
• 页码: 1878-1893
• 关键词: linear models; i-projections

摘要

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.