2013/05/07 by Forrest W. Crawford, Crawford, Forrest W., Daniel Zelterman +1
Mathematics · #Bernoulli's principle #Binary data #Binary number #Cluster (spacecraft) #Computer science #Covariate #FOS: Computer and information sciences #FOS: Mathematics #Markov chain #Markov model #Mathematics #Methodology (stat.ME) #Statistical Methods and Bayesian Inference #Statistical Methods and Inference #Statistical Methods in Clinical Trials #Statistics #Statistics Theory (math.ST) #math.ST #stat.ME #stat.TH
paper · pdf · doi:10.48550/arxiv.1305.1656
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2013/05/07 · arxiv created 2014/08/27 · arxiv updated 2014/08/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
We propose a class of continuous-time Markov counting processes for analyzing correlated binary data and establish a correspondence between these models and sums of exchangeable Bernoulli random variables. Our approach generalizes many previous models for correlated outcomes, admits easily interpretable parameterizations, allows different cluster sizes, and incorporates ascertainment bias in a natural way. We demonstrate several new models for dependent outcomes and provide algorithms for computing maximum likelihood estimates. We show how to incorporate cluster-specific covariates in a regression setting and demonstrate improved fits to well-known datasets from familial disease epidemiology and developmental toxicology.