Discrete Exponential-Family Models for Multivariate Binary Outcomes
Methodology
2022-11-03 v2 Computation
Abstract
Studies that collect multi-outcome data such as tobacco and alcohol use are becoming increasingly common. In principle, multi-outcomes studies investigate the correlations between outcomes, including, causal links and/or joint distributions. Although there are many methods for studying multivariate outcomes, significant limitations regarding scale and interpretation persist. Here we introduce a model based on the exponential-family for discrete binary outcomes that provides a flexible framework for hypothesis testing of multiple binary outcomes in a computationally efficient fashion.
Cite
@article{arxiv.2211.00627,
title = {Discrete Exponential-Family Models for Multivariate Binary Outcomes},
author = {George G. Vega Yon and Mary Jo Pugh and Thomas W. Valente},
journal= {arXiv preprint arXiv:2211.00627},
year = {2022}
}