A CONSISTENT MODEL SELECTION PROCEDURE FOR MARKOV RANDOM FIELDS BASED ON PENALIZED PSEUDOLIKELIHOOD
成果类型：
Article
署名作者：
Ji, Chuanshu; Seymour, Lynne
署名单位：
University of North Carolina; University of North Carolina Chapel Hill; University System of Georgia; University of Georgia
刊物名称：
ANNALS OF APPLIED PROBABILITY
ISSN/ISSBN：
1050-5164
发表日期：
1996
页码：
423-443
关键词：
摘要：
Motivated by applications in texture synthesis, we propose a model selection procedure for Markov random fields based on penalized pseudo-likelihood. The procedure is shown to be consistent for choosing the true model, even for Gibbs random fields with phase transitions. As a by-product, rates for the restricted mean-square error and moderate deviation probabilities are derived for the maximum pseudolikelihood estimator. Some simulation results are presented for the selection procedure.