论文标题
多元二进制结果的离散指数家庭模型
Discrete Exponential-Family Models for Multivariate Binary Outcomes
论文作者
论文摘要
收集多种结果数据（例如烟草和饮酒）的研究变得越来越普遍。原则上，多进取的研究研究了结果之间的相关性，包括因果关系和/或关节分布。尽管有许多研究多元结果的方法，但有关规模和解释的重大局限性持续存在。在这里，我们介绍了一个基于指数家庭的模型，用于离散的二进制结果，该结果为以计算有效的方式对多个二元结果进行假设测试提供了灵活的框架。
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.
