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Fast Multivariate Probit Estimation via a Two-Stage Composite Likelihood

Author

Listed:
  • Bryan Ting

    (North Carolina State University)

  • Fred Wright

    (North Carolina State University
    North Carolina State University)

  • Yi-Hui Zhou

    (North Carolina State University
    North Carolina State University
    North Carolina State University)

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.

Suggested Citation

  • Bryan Ting & Fred Wright & Yi-Hui Zhou, 2022. "Fast Multivariate Probit Estimation via a Two-Stage Composite Likelihood," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 14(3), pages 533-549, December.
    • Handle: RePEc:spr:stabio:v:14:y:2022:i:3:d:10.1007_s12561-022-09338-6
    DOI: 10.1007/s12561-022-09338-6
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    References listed on IDEAS

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