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Accelerating computation: A pairwise fitting technique for multivariate probit models

Author

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  • Delporte, Margaux
  • Verbeke, Geert
  • Fieuws, Steffen
  • Molenberghs, Geert

Abstract

Fitting multivariate probit models via maximum likelihood presents considerable computational challenges, particularly in terms of computation time and convergence difficulties, even for small numbers of responses. These issues are exacerbated when dealing with ordinal data. An efficient computational approach is introduced, based on a pairwise fitting technique within a pseudo-likelihood framework. This methodology is applied to clinical case studies, specifically using a trivariate probit model. Additionally, the correlation structure among outcomes is allowed to depend on covariates, enhancing both the flexibility and interpretability of the model. By way of simulation and real data applications, the proposed approach demonstrates superior computational efficiency as the dimension of the outcome vector increases. The method's ability to capture covariate-dependent correlations makes it particularly useful in medical research, where understanding complex associations among health outcomes is of scientific importance.

Suggested Citation

  • Delporte, Margaux & Verbeke, Geert & Fieuws, Steffen & Molenberghs, Geert, 2025. "Accelerating computation: A pairwise fitting technique for multivariate probit models," Computational Statistics & Data Analysis, Elsevier, vol. 203(C).
    • Handle: RePEc:eee:csdana:v:203:y:2025:i:c:s016794732400166x
    DOI: 10.1016/j.csda.2024.108082
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    References listed on IDEAS

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    1. Steffen Fieuws & Geert Verbeke, 2006. "Pairwise Fitting of Mixed Models for the Joint Modeling of Multivariate Longitudinal Profiles," Biometrics, The International Biometric Society, vol. 62(2), pages 424-431, June.
    2. Steffen Fieuws & Geert Verbeke & Filip Boen & Christophe Delecluse, 2006. "High dimensional multivariate mixed models for binary questionnaire data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 55(4), pages 449-460, August.
    3. Morimune, Kimio, 1979. "Comparisons of Normal and Logistic Models in the Bivariate Dichotomous Analysis," Econometrica, Econometric Society, vol. 47(4), pages 957-975, July.
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