Near universal consistency of the maximum pseudolikelihood estimator for discrete models

被引:2
|
作者
Nguyen, Hien D. [1 ]
机构
[1] La Trobe Univ, Dept Math & Stat, Melbourne, Vic, Australia
基金
澳大利亚研究理事会;
关键词
Consistency; Discrete models; Maximum pseudolikelihood estimation; Nonparametrics; LIKELIHOOD;
D O I
10.1016/j.jkss.2017.10.001
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
Maximum pseudolikelihood (MPL) estimators are useful alternatives to maximum likelihood (ML) estimators when likelihood functions are more difficult to manipulate than their marginal and conditional components. Furthermore, MPL estimators subsume a large number of estimation techniques including ML estimators, maximum composite marginal likelihood estimators, and maximum pairwise likelihood estimators. When considering only the estimation of discrete models (on a possibly countably infinite support), we show that a simple finiteness assumption on an entropy-based measure is sufficient for assessing the consistency of the MPL estimator. As a consequence, we demonstrate that the MPL estimator of any discrete model on a bounded support will be consistent. Our result is valid in parametric, semiparametric, and nonparametric settings. (C) 2017 The Korean Statistical Society. Published by Elsevier B.V. All rights reserved.
引用
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页码:90 / 98
页数:9
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