Maximum likelihood estimation in Gaussian chain graph models under the alternative Markov property

M. Drton*, M. Eichler

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

20 Citations (Web of Science)


The andersson–madigan–perlman (amp) markov property is a recently proposed alternative markov property (amp) for chain graphs. In the case of continuous variables with a joint multivariate gaussian distribution, it is the amp rather than the earlier introduced lauritzen–wermuth–frydenberg markov property that is coherent with data-generation by natural block-recursive regressions. In this paper, we show that maximum likelihood estimates in gaussian amp chain graph models can be obtained by combining generalized least squares and iterative proportional fitting to an iterative algorithm. In an appendix, we give useful convergence results for iterative partial maximization algorithms that apply in particular to the described algorithm.
Original languageEnglish
Pages (from-to)247-257
Number of pages10
JournalScandinavian Journal of Statistics
Publication statusPublished - 1 Jan 2006

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