A hybrid EM/Gauss-Newton algorithm for maximum likelihood in mixture distributions

被引：29

作者：

Aitkin, M

Aitkin, I

机构：

[1] UNIV WESTERN AUSTRALIA,DEPT MATH,NEWCASTLE TYNE,TYNE & WEAR,ENGLAND

[2] UNIV NEWCASTLE UPON TYNE,DEPT STAT,NEWCASTLE TYNE,TYNE & WEAR,ENGLAND

[3] CURTIN UNIV TECHNOL,SCH PUBL HLTH,DEPT EPIDEMIOL & BIOSTAT,BENTLEY,WA 6102,AUSTRALIA

来源：

STATISTICS AND COMPUTING | 1996年 / 6卷 / 02期

关键词：

mixtures; maximum likelihood; Gauss-Newton; EM algorithm;

D O I：

10.1007/BF00162523

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

A faster alternative to the EM algorithm in finite mixture distributions is described, which alternates EM iterations with Gauss-Newton iterations using the observed information matrix. At the expense of modest additional analytical effort in obtaining the observed information, the hybrid algorithm reduces the computing time required and provides asymptotic standard errors at convergence. The algorithm is illustrated on the two-component normal mixture.

引用

页码：127 / 130

页数：4

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