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Properties of graphical regression models for multidimensional categorical data

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  • Johnson, Devin S.
  • Hoeting, Jennifer A.

Abstract

We propose a two-component graphical chain model, the discrete regression distribution, where a set of discrete random variables is modeled as a response to a set of categorical and continuous covariates. The proposed model is useful for modeling a set of discrete variables measured at multiple sites along with a set of continuous and/or discrete covariates. The proposed model allows for joint examination of the dependence structure of the discrete response and observed covariates and also accommodates site-to-site variability. We develop the graphical model properties and theoretical justifications of this model. Our model has several advantages over the traditional logistic normal model used to analyze similar compositional data, including site-specific random effect terms and the incorporation of discrete and continuous covariates.

Suggested Citation

  • Johnson, Devin S. & Hoeting, Jennifer A., 2011. "Properties of graphical regression models for multidimensional categorical data," Statistics & Probability Letters, Elsevier, vol. 81(10), pages 1471-1475, October.
    • Handle: RePEc:eee:stapro:v:81:y:2011:i:10:p:1471-1475
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    References listed on IDEAS

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    1. Steen A. Andersson & David Madigan & Michael D. Perlman, 2001. "Alternative Markov Properties for Chain Graphs," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 28(1), pages 33-85, March.
    2. Billheimer D. & Guttorp P. & Fagan W.F., 2001. "Statistical Interpretation of Species Composition," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1205-1214, December.
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