Counting processes for correlated binary responses
Year: 2013
Type: preprint
Abstract: 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 dependent Bernoulli random variables using a technique called “probabilistic embedding”. Our approach generalizes many previous models for corre-lated outcomes, admits easily interpretable parameterizations, allows different cluster sizes, incorporates ascertainment bias in a natural way, and dramatically simplifies likelihood-based inference. We demon-strate several new models for dependent outcomes, derive explicit likelihood expressions, and provide algorithms for computing maximum likelihood estimates. We show how to incorporate cluster-specific covariates in a regression setting and apply our method to well-known problems from developmental toxicology and familial disease epidemiology.
Source: arXiv (Cornell University)
Authors: Forrest W. Crawford, Daniel Zelterman
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W40537428