One of the most powerful algorithms for obtaining maximum likelihood estimates for many incomplete-data problems is the EM algorithm.However,when the parameters satisfy a set of nonlinear restrictions,It is difficult ...One of the most powerful algorithms for obtaining maximum likelihood estimates for many incomplete-data problems is the EM algorithm.However,when the parameters satisfy a set of nonlinear restrictions,It is difficult to apply the EM algorithm directly.In 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 used.Essentially this kind of estimation problem is a stochastic optimization problem in the M-step.We make use of methods in stochastic optimization to overcome the difficulty caused by nonlinearity in the given constraints.展开更多
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)+1 种基金Henan Big Data Double Base of Zhengzhou Institute of Technology(20174101546503022265)the Key Scientific Research Foundation of Education Bureau of Henan Province(20B110020)
文摘One of the most powerful algorithms for obtaining maximum likelihood estimates for many incomplete-data problems is the EM algorithm.However,when the parameters satisfy a set of nonlinear restrictions,It is difficult to apply the EM algorithm directly.In 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 used.Essentially this kind of estimation problem is a stochastic optimization problem in the M-step.We make use of methods in stochastic optimization to overcome the difficulty caused by nonlinearity in the given constraints.
