Statistics and Computing

, Volume 6, Issue 2, pp 127–130

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

  • Murray Aitkin
  • Irit Aitkin

DOI: 10.1007/BF00162523

Cite this article as:
Aitkin, M. & Aitkin, I. Stat Comput (1996) 6: 127. doi:10.1007/BF00162523


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.


Mixturesmaximum likelihoodGauss-NewtonEM algorithm

Copyright information

© Chapman & Hall 1996

Authors and Affiliations

  • Murray Aitkin
    • 1
    • 2
  • Irit Aitkin
    • 3
  1. 1.Department of MathematicsUniversity of Western AustraliaWestern AustraliaAustralia
  2. 2.Department of StatisticsUniversity of Newcastle Upon TyneNewcastle Upon TyneUK
  3. 3.Department of Epidemiology and BiostatisticsSchool of Public Health, Curtin University of TechnologyBentleyAustralia