Estimation of high-dimensional partially-observed discrete Markov random fields

被引：3
|
作者：
Atchade, Yves F. [1 ]
机构：
[1] Univ Michigan, Ann Arbor, MI 48109 USA
来源：
ELECTRONIC JOURNAL OF STATISTICS | 2014年 / 8卷
关键词：
Network estimation; high-dimensional inference; penalized likelihood inference; misspecification; pseudo-likelihood; Markov random fields; MODEL SELECTION; LIKELIHOOD-ESTIMATION; LOGISTIC-REGRESSION; GRAPHICAL MODEL; LASSO;
D O I：
10.1214/14-EJS946
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：
We consider the problem of estimating the parameters of discrete Markov random fields from partially observed data in a high-dimensional setting. Using a,l(1)-penalized pseudo-likelihood approach, we fit a misspecified model obtained by ignoring the missing data problem. We derive an estimation error bound that highlights the effect of the misspecification. We report some simulation results that illustrate the theoretical findings.
引用
收藏
页码：2242 / 2263
页数：22
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