文章基本信息

标题：An oracle approach for interaction neighborhood estimation in random fields
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作者：Matthieu Lerasle ; Daniel Y. Takahashi
期刊名称：Electronic Journal of Statistics
印刷版ISSN：1935-7524
出版年度：2011
卷号：5
页码：534-571
DOI：10.1214/11-EJS618
语种：English
出版社：Institute of Mathematical Statistics
摘要：We consider the problem of interaction neighborhood estimation from the partial observation of a finite number of realizations of a random field. We introduce a model selection rule to choose estimators of conditional probabilities among natural candidates. Our main result is an oracle inequality satisfied by the resulting estimator. We use then this selection rule in a two-step procedure to evaluate the interacting neighborhoods. The selection rule selects a small prior set of possible interacting points and a cutting step remove from this prior set the irrelevant points.
关键词：Ising model;model selection;computationally efficient algorithm.