Mixture modeling with applications in schizophrenia research

作     者：Wu, Qiang Sampson, Allan R. 

作者机构：E Carolina Univ Dept Biostat Greenville NC 27858 USA Univ Pittsburgh Dept Stat Pittsburgh PA 15260 USA 

出 版 物：《COMPUTATIONAL STATISTICS & DATA ANALYSIS》 (计算统计学与数据分析)

年 卷 期：2009年第53卷第7期

页      面：2563-2572页

核心收录：

学科分类：08[工学] 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：NIMH [1 P50MH084053-01] 

主　　题：SCHIZOPHRENIA RESEARCH MATHEMATICAL models EXPECTATION-maximization algorithms CLUSTER analysis (Statistics) ESTIMATION theory MATHEMATICAL statistics 

摘      要：Finite mixture modeling, together with the EM algorithm, have been widely used in clustering analysis. Under such methods, the unknown group membership is usually treated as missing data. When the complete data (log-)likelihood function does not have an explicit solution, the simplicity of the EM algorithm breaks down. Authors, including Rai and Matthews [Rai, S.N., Matthews, D.E., 1993. Improving the em algorithm. Biometrics 49, 587-591], Lange [Lange, K., 1995a. A gradient algorithm locally equivalent to the em algorithm. Journal of the Royal Statistical Society B 57(2) 425-437], and Titterington [Titterington, D.M., 1984. Recursive parameter estimation using incomplete data. journal of the Royal Statistical Society B. 46, 257-267] developed modified algorithms therefore. As motivated by research in a large neurobiological project, we propose in this paper a new variant of such modifications and show that it is self-consistent. Moreover, simulations are conducted to demonstrate that the new variant converges faster than its predecessors. (C) 2008 Elsevier B.V. All rights reserved.