An Improved Approach for Estimating Empirical likelihood Based on Random Walk Metropolis Algorithm

摘要：

　　It has been proved that empirical likelihood can be a likelihood function for Bayesian analysis.Based on this discovery,we propose a random walk metropolis algorithm to estimate the maximum empirical likelihood.First,a simulation dataset is generated which take a uniform distribution as prior,and then obtain a posterior.Finally,we prove this distribution is the normal one and the max value is similar to empirical likelihood estimator by sequential quadratic programming.

关键词： Estimation Bayesian analysis Empirical likelihood Random walk Metropolis Algorithm

作者: LIU Ling CHENG Dixiang YI Dong

作者单位: Department of biostatistics,The Third Military Medical University,Chongqing,P.R.China,400038 School of Software Engineering,Chongqing University of Posts and Telecommunications,Chongqing,P.R.Ch

会议类型: 国际会议

会议名称: The 5th International Institute of Statistics & Management Engineering Symposium (第五届(2012)国际统计与管理工程学术研讨会(IISMES2012))

会议地点: 青岛

会议语种:英文

页码: 92-98

在线出版日期: 2012-07-20（万方平台首次上网日期，不代表论文的发表时间）