An empirical comparison of EM, SEM and MCMC performance for problematic Gaussian mixture likelihoods

作     者：Dias, JG Wedel, M 

作者机构：ISCTE Dept Quantitat Methods P-1649026 Lisbon Portugal Univ Michigan Sch Business Ann Arbor MI 48109 USA 

出 版 物：《STATISTICS AND COMPUTING》 (统计学与计算)

年 卷 期：2004年第14卷第4期

页      面：323-332页

核心收录：

学科分类：0202[经济学-应用经济学] 02[经济学] 020208[经济学-统计学] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：¸ãoparaaCiência e Tecnologia 

主　　题：Gaussian mixture models EM algorithm SEM algorithm MCMC label switching loss functions conjugate prior hierarchical prior 

摘      要：We compare EM, SEM, and MCMC algorithms to estimate the parameters of the Gaussian mixture model. We focus on problems in estimation arising from the likelihood function having a sharp ridge or saddle points. We use both synthetic and empirical data with those features. The comparison includes Bayesian approaches with different prior specifications and various procedures to deal with label switching. Although the solutions provided by these stochastic algorithms are more often degenerate, we conclude that SEM and MCMC may display faster convergence and improve the ability to locate the global maximum of the likelihood function.