Fast Multivariate Probit Estimation via a Two-Stage Composite Likelihood

2020/04/20 by Bryan W. Ting, Fred A. Wright, Ting, Bryan W. +3
Biochemistry, Genetics and Molecular Biology · #Computation (stat.CO) #FOS: Computer and information sciences #Gene expression and cancer classification #Genetic Mapping and Diversity in Plants and Animals #Genetic and phenotypic traits in livestock #Methodology (stat.ME)

paper · pdf · doi:10.48550/arxiv.2004.09623

openalex publication_date 2020/04/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

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