The EM algorithm for ML Estimators under nonlinear inequalities restrictions on the parameters

作     者：SHEN Qi-xia MIAO Peng LIANG Yin-shuang 

作者机构：Department of Basic CourseZhengzhou University of Science and TechnologyZhengzhou 450064China Information Engineering School of Zhengzhou Institute of TechnologyZhengzhou 450044China 

出 版 物：《Applied Mathematics(A Journal of Chinese Universities)》 (高校应用数学学报（英文版）（B辑）)

年 卷 期：2019年第34卷第4期

页      面：393-402页

核心收录：

学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学] 

基　　金：Supported by Teaching reform project of Zhengzhou University of Science and Technology(KFCZ201909) National Foundation for Cultivating Scientific Research Projects of Zhengzhou Institute of Technology(GJJKTPY2018K4) Henan Big Data Double Base of Zhengzhou Institute of Technology(20174101546503022265) the Key Scientific Research Foundation of Education Bureau of Henan Province(20B110020) 

主　　题：Linear regression Maximum likelihood estimation Nonlinear constraints Asymptotic properties 

摘      要：One of the most powerful algorithms for obtaining maximum likelihood estimates for many incomplete-data problems is the EM ***,when the parameters satisfy a set of nonlinear restrictions,It is difficult to apply the EM algorithm *** this paper,we propose an asymptotic maximum likelihood estimation procedure under a set of nonlinear inequalities restrictions on the parameters,in which the EM algorithm can be *** this kind of estimation problem is a stochastic optimization problem in the *** make use of methods in stochastic optimization to overcome the difficulty caused by nonlinearity in the given constraints.