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Labelled Graphical Models

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  • Jukka Corander

Abstract

A class of log‐linear models, referred to as labelled graphical models (LGMs), is introduced for multinomial distributions. These models generalize graphical models (GMs) by employing partial conditional independence restrictions which are valid only in subsets of an outcome space. Theoretical results concerning model identifiability, decomposability and estimation are derived. A decision theoretical framework and a search algorithm for the identification of plausible models are described. Real data sets are used to illustrate that LGMs may provide a simpler interpretation of a dependence structure than GMs.

Suggested Citation

  • Jukka Corander, 2003. "Labelled Graphical Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 30(3), pages 493-508, September.
    • Handle: RePEc:bla:scjsta:v:30:y:2003:i:3:p:493-508
    DOI: 10.1111/1467-9469.00344
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    Cited by:

    1. Henrik Nyman & Johan Pensar & Timo Koski & Jukka Corander, 2016. "Context-specific independence in graphical log-linear models," Computational Statistics, Springer, vol. 31(4), pages 1493-1512, December.
    2. Johan Pensar & Henrik Nyman & Jukka Corander, 2017. "Structure Learning of Contextual Markov Networks using Marginal Pseudo-likelihood," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 44(2), pages 455-479, June.

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