A CONSISTENT MODEL SELECTION PROCEDURE FOR MARKOV RANDOM FIELDS BASED ON PENALIZED PSEUDOLIKELIHOOD

被引：0

作者：

Ji, Chuanshu ^{[1
]}

Seymour, Lynne ^{[2
]}

机构：

[1] Univ North Carolina, Dept Stat, Chapel Hill, NC 27599 USA

[2] Univ Georgia, Dept Stat, Athens, GA 30602 USA

来源：

ANNALS OF APPLIED PROBABILITY | 1996年 / 6卷 / 02期

关键词：

Markov random fields; Gibbs random fields; model selection; pseudo; likelihood; texture synthesis; image analysis;

D O I：

暂无

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

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.

引用

页码：423 / 443

页数：21

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