Graphical models for multivariate Markov chains

DOI10.1016/j.jmva.2012.01.010zbMath1241.60036OpenAlexW1972818238MaRDI QIDQ413752

Sabrina Giordano, Roberto Colombi

Publication date: 7 May 2012

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmva.2012.01.010

zbMATH Keywords

conditional independenceGranger noncausalitygeneralized marginal interactions

Mathematics Subject Classification ID

Markov chains (discrete-time Markov processes on discrete state spaces) (60J10)

Related Items (2)

Monotone dependence in graphical models for multivariate Markov chains ⋮ Multiple hidden Markov models for categorical time series

Uses Software

• R
• hmmm
• MIM
• mph.fit

Cites Work

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• Granger causality and path diagrams for multivariate time series
• Non-causality in bivariate binary time series
• Monotone dependence in graphical models for multivariate Markov chains
• Discrete chain graph models
• Chain graph models of multivariate regression type for categorical data
• Alternative Markov Properties for Chain Graphs
• Parameterizations and Fitting of Bi‐directed Graph Models to Categorical Data
• Tests of Noncausality under Markov Assumptions for Qualitative Panel Data
• The General Equivalence of Granger and Sims Causality
• Markov Properties for Acyclic Directed Mixed Graphs
• On the Validity of the Markov Interpretation of Path Diagrams of Gaussian Structural Equations Systems with Correlated Errors
• Multinomial-Poisson models subject to inequality constraints
• Investigating Causal Relations by Econometric Models and Cross-spectral Methods
• Homogeneous Linear Predictor Models for Contingency Tables

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