Fast Multivariate Probit Estimation via a Two-Stage Composite Likelihood

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. C...

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Bibliographic Details
Published in:Statistics in biosciences 2022-12, Vol.14 (3), p.533-549
Main Authors: Ting, Bryan, Wright, Fred, Zhou, Yi-Hui
Format: Article
Language:English
Subjects:
Binary data
Biostatistics
Computational efficiency
Computer applications
Computing time
Health Sciences
Mathematics and Statistics
Medicine
Modelling
Multivariate analysis
Precision medicine
Statistics
Statistics for Life Sciences
Theoretical Ecology/Statistics
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Summary: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.
ISSN:1867-1764
1867-1772
DOI:10.1007/s12561-022-09338-6