论文标题
通过两阶段的复合可能性估算快速多元概率估计
Fast Multivariate Probit Estimation via a Two-Stage Composite Likelihood
论文作者
论文摘要
多元概率在建模相关的二进制数据方面很受欢迎，具有灵活性和简单性的吸引力。但是，在计算和设计明确的统计框架中仍然存在着巨大的挑战。近年来，对多元概率的兴趣增加了。当前的应用包括基因组学和精度医学，其中可能具有多种特征的同时建模，而计算效率是一个重要的考虑因素。我们提出了一种通过两阶段复合可能性估计多元概率估计的快速方法。我们探索计算效率和统计效率，并注意该方法为纯二进制设置以外的扩展设定了阶段。
The multivariate probit is popular for modeling correlated binary data, with an attractive balance of flexibility and simplicity. However, considerable challenges remain in computation and in devising a clear statistical framework. Interest in the multivariate probit has increased in recent years. Current applications include genomics and precision medicine, where simultaneous modeling of multiple traits may be of interest, and computational efficiency is an important consideration. We propose a fast method for multivariate probit estimation via a two-stage composite likelihood. We explore computational and statistical efficiency, and note that the approach sets the stage for extensions beyond the purely binary setting.
