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Marginal log-linear parameters for graphical Markov models.

Abstract:

Marginal log-linear (MLL) models provide a flexible approach to multivariate discrete data. MLL parametrizations under linear constraints induce a wide variety of models, including models defined by conditional independences. We introduce a subclass of MLL models which correspond to Acyclic Directed Mixed Graphs (ADMGs) under the usual global Markov property. We characterize for precisely which graphs the resulting parametrization is variation independent. The MLL approach provides the first ...

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Publication status:
Published

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Publisher copy:
10.1111/rssb.12020

Authors


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Institution:
University of Oxford
Department:
Oxford, MPLS, Statistics
Richardson, TS More by this author
Journal:
Journal of the Royal Statistical Society. Series B, Statistical methodology
Volume:
75
Issue:
4
Pages:
743-768
Publication date:
2013-09-05
DOI:
EISSN:
1467-9868
ISSN:
1369-7412
URN:
uuid:2dab81ba-dbb5-44fc-bc22-4cca34d515f0
Source identifiers:
425325
Local pid:
pubs:425325

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