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On the occasional exactness of the distributional transform approximation for direct Gaussian copula models with discrete margins

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  • Hughes, John

Abstract

The direct Gaussian copula model with discrete margins is appealing but poses computational challenges due to its intractable likelihood. We show that the distributional transform-based approximate likelihood is essentially exact for some variants of the model, and we propose a quantity that can be used to assess exactness for a given dataset.

Suggested Citation

  • Hughes, John, 2021. "On the occasional exactness of the distributional transform approximation for direct Gaussian copula models with discrete margins," Statistics & Probability Letters, Elsevier, vol. 177(C).
    • Handle: RePEc:eee:stapro:v:177:y:2021:i:c:s0167715221001218
    DOI: 10.1016/j.spl.2021.109159
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    References listed on IDEAS

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    1. L. Madsen & Y. Fang, 2011. "Joint Regression Analysis for Discrete Longitudinal Data," Biometrics, The International Biometric Society, vol. 67(3), pages 1171-1175, September.
    2. White, Halbert, 1982. "Maximum Likelihood Estimation of Misspecified Models," Econometrica, Econometric Society, vol. 50(1), pages 1-25, January.
    3. Denuit, Michel & Lambert, Philippe, 2005. "Constraints on concordance measures in bivariate discrete data," Journal of Multivariate Analysis, Elsevier, vol. 93(1), pages 40-57, March.
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