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A Subcopula Characterization of Dependence for the Multivariate Bernoulli Distribution

Arturo Erdely

Journal of Statistical Theory and Applications2026https://doi.org/10.1007/s44199-025-00157-4article
ABDC C
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0.50

What the paper says

This paper provides a subcopula characterization of dependence for the Multivariate Bernoulli Distribution. Explicit formulas are derived using subcopulas to introduce dependence measures for interactions of all orders, not just pairwise. A Bayesian inference approach is also applied to estimate the parameters, offering practical tools for parameter estimation and dependence analysis in real-world applications. The main contribution is an explicit, order-by-order characterization of multivariate dependence for binary data (beyond pairwise association), linking joint probabilities to subcopula-based dependence parameters/measures under full compatibility; this fills a gap in the literature where practical modeling often stops at pairwise dependence.

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https://doi.org/https://doi.org/10.1007/s44199-025-00157-4

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@article{arturo2026,
  title        = {{A Subcopula Characterization of Dependence for the Multivariate Bernoulli Distribution}},
  author       = {Arturo Erdely},
  journal      = {Journal of Statistical Theory and Applications},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1007/s44199-025-00157-4},
}

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A Subcopula Characterization of Dependence for the Multivariate Bernoulli Distribution

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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