Iterative conditional fitting for discrete chain graph models

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3 Scopus citations

Abstract

'Iterative conditional fitting' is a recently proposed algorithm that can be used for maximization of the likelihood function in marginal independence models for categorical data. This paper describes a modification of this algorithm, which allows one to compute maximum likelihood estimates in a class of chain graph models for categorical data. The considered discrete chain graph models are defined using conditional independence relations arising in recursive multivariate regressions with correlated errors. This Markov interpretation of the chain graph is consistent with treating the graph as a path diagram and differs from other interpretations known as the LWF and AMP Markov properties.

Original languageEnglish
Title of host publicationCOMPSTAT 2008 - Proceedings in Computational Statistics, 18th Symposium
PublisherSpringer Berlin Heidelberg
Pages93-104
Number of pages12
ISBN (Print)9783790820836
DOIs
StatePublished - 2008
Externally publishedYes
Event18th Symposium on Computational Statistics, COMPSTAT 2008 - Porto, Portugal
Duration: 24 Aug 200829 Aug 2008

Publication series

NameCOMPSTAT 2008 - Proceedings in Computational Statistics, 18th Symposium

Conference

Conference18th Symposium on Computational Statistics, COMPSTAT 2008
Country/TerritoryPortugal
CityPorto
Period24/08/0829/08/08

Keywords

  • Categorical data
  • Chain graph
  • Conditional independence
  • Graphical model

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