A divide-and-conquer approach in applying EM for large recursive models with incomplete categorical data
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Publication:959190
DOI10.1016/j.csda.2004.09.006zbMath1431.62244OpenAlexW2037996913MaRDI QIDQ959190
Publication date: 11 December 2008
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.csda.2004.09.006
consistency of distributionD-splitfamily conditionhyper-EM conditionhyper-EM graphjunction tree of submodelsnode removabilityT-split
Computational methods for problems pertaining to statistics (62-08) Bayesian inference (62F15) Contingency tables (62H17)
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Cites Work
- The EM algorithm for graphical association models with missing data
- On the convergence properties of the EM algorithm
- Markov fields and log-linear interaction models for contingency tables
- Product models for frequency tables involving indirect observation
- Influence diagrams for statistical modelling
- Hyper Markov laws in the statistical analysis of decomposable graphical models
- A characterization of Markov equivalence classes for acyclic digraphs
- Hyper-EM for large recursive models of categorical variables.
- Calibrated initials for an EM applied to recursive models of categorical variables.
- Evidence and inference in educational assessment
- The TM algorithm for maximising a conditional likelihood function
- Graphical and Recursive Models for Contingency Tables
- Collapsibility and response variables in contingency tables
- On the Markov Equivalence of Chain Graphs, Undirected Graphs, and Acyclic Digraphs
- Independence properties of directed markov fields
- Exploratory latent structure analysis using both identifiable and unidentifiable models
- An algebra of bayesian belief universes for knowledge‐based systems
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