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Unpublished Paper
A unified approach to modeling multivariate binary data using copulas over partitions
Johns Hopkins University, Dept. of Biostatistics Working Papers
  • Bruce J. Swihart, Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health
  • Brian Caffo, Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health
  • Ciprian Crainiceanu, Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health
Date of this Version
7-15-2010
Abstract

Many seemingly disparate approaches for marginal modeling have been developed in recent years. We demonstrate that many current approaches for marginal modeling of correlated binary outcomes produce likelihoods that are equivalent to the proposed copula-based models herein. These general copula models of underlying latent threshold random variables yield likelihood based models for marginal fixed effects estimation and interpretation in the analysis of correlated binary data. Moreover, we propose a nomenclature and set of model relationships that substantially elucidates the complex area of marginalized models for binary data. A diverse collection of didactic mathematical and numerical examples are given to illustrate concepts.

Citation Information
Bruce J. Swihart, Brian Caffo and Ciprian Crainiceanu. "A unified approach to modeling multivariate binary data using copulas over partitions" (2010)
Available at: http://works.bepress.com/ciprian_crainiceanu/20/