Parameterizations and Fitting of Bi‐directed Graph Models to Categorical Data

DOI10.1111/j.1467-9469.2008.00638.xzbMath1196.62074arXiv0801.1440OpenAlexW1965733286MaRDI QIDQ3552973

Giovanni M. Marchetti, Monia Lupparelli, Wicher P. Bergsma

Publication date: 22 April 2010

Published in: Scandinavian Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/0801.1440

zbMATH Keywords

maximum likelihood estimationcontingency tableslog-linear parametrizationlogistic parametrization

Mathematics Subject Classification ID

Related Items (11)

Palindromic Bernoulli distributions ⋮ Composite mixture of log-linear models with application to psychiatric studies ⋮ Discrete chain graph models ⋮ Hierarchical models for independence structures of networks ⋮ Sequences of regressions and their independences ⋮ Graphical models for multivariate Markov chains ⋮ Chain graph models of multivariate regression type for categorical data ⋮ Monotone Graphical Multivariate Markov Chains ⋮ Conjugate and conditional conjugate Bayesian analysis of discrete graphical models of marginal independence ⋮ Probability based independence sampler for Bayesian quantitative learning in graphical log-linear marginal models ⋮ Log‐mean Linear Parameterization for Discrete Graphical Models of Marginal Independence and the Analysis of Dichotomizations

Uses Software

• R
• MIM

Cites Work

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• A NOTE ON MULTIVARIATE LOGISTIC MODELS FOR CONTINGENCY TABLES
• Markov Properties for Acyclic Directed Mixed Graphs
• Introduction to Graphical Modelling
• An approximate maximum likelihood procedure for parameter estimation in multivariate discrete data regression models

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