Directional Dependence via Copulas: Examples and Applications in Engineering


Autores/as

DOI:

https://doi.org/10.22517/23447214.25954

Palabras clave:

Dependencia direccional, Modelos de cópulas, Dependencia en colas, Eventos extremos, Ingeniería hidráulica, Ingeniería estructural, Modelado multivariado

Resumen

Resumen— En muchos sistemas ingenieriles, las variables de interés no solo presentan correlaciones promedio, sino que su relación cambia significativamente en situaciones extremas. La dependencia direccional se refiere a la capacidad de capturar esas relaciones asimétricas, especialmente en las colas superiores (eventos máximos) o inferiores (mínimos). Las cópulas ofrecen una herramienta flexible para modelar dicha dependencia, independientemente de las distribuciones marginales de cada variable.

Este artículo tiene como objetivo divulgar este fenómeno, explicar cómo se mide, qué herramientas se usan, y mostrar dos aplicaciones reales: una en ingeniería hidráulica (precipitaciones extremas) y otra en ingeniería estructural (cargas combinadas extremas). También se proveen códigos en R y gráficas de alta resolución para que otros ingenieros puedan reproducir los análisis.

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Citas

T. Morais, G. A. Barberes, I. V. A. F. Souza, F. G. Leal, J. V. P. Guzzo, and A. L. D. Spigolon, “Pearson correlation coefficient applied to petroleum system characterization: The case study of Potiguar and Recôncavo basins, Brazil,” Geosciences, vol. 13, no. 9, p. 282, 2023.

J. Rymarz, M. Rymarz, T. Uhl, and P. Uhl, “Application of Spearman’s method for the analysis of vehicle downtime and age on availability,” Applied Sciences, vol. 12, no. 6, p. 2921, 2022.

E. A. Sungur and S. Çelebioğlu, “Copulas with directional dependence property,” Gazi University Journal of Science, vol. 24, no. 3, pp. 415–424, 2011.

C. Spearman, “The proof and measurement of association between two things,” American Journal of Psychology, vol. 15, no. 1, pp. 72–101, 1904.

K. Pearson, “Notes on regression and inheritance in the case of two parents,” Proceedings of the Royal Society of London, vol. 58, pp. 240–242, 1895.

P. Schober, C. Boer, and L. A. Schwarte, “Correlation coefficients: Appropriate use and interpretation,” Anesthesia & Analgesia, vol. 126, no. 5, pp. 1763–1768, 2018.

T. Afrin, A. S. Iquebal, M. Karimi, A. Souris, S. Y. Lee, and B. K. Mallick, “Directionally dependent multi-view clustering using copula model,” PLOS ONE, vol. 15, no. 10, e0238996, 2020.

D. Kim and J. M. Kim, “Analysis of directional dependence using asymmetric copula-based regression models,” Journal of Statistical Computation and Simulation, vol. 84, no. 9, pp. 1990–2010, 2014.

E. Liebscher, “Construction of asymmetric multivariate copulas,” Journal of Multivariate Analysis, vol. 99, no. 10, pp. 2234–2250, 2008.

Y. S. Jung, J. M. Kim, and J. Kim, “New approach of directional dependence in exchange markets using generalized FGM copula function,” Communications in Statistics: Simulation and Computation, vol. 37, no. 4, pp. 772–788, 2008.

S. Huang, X. Chen, Y. Zhang, and H. Li, “Copula-based estimation of directional extreme wind speeds,” Science of the Total Environment, In Press, 2024.

X. Zhang, L. Zhi, and X. Li, “Influence of dependence of directional extreme wind speeds when estimating the wind load,” Journal of Wind Engineering & Industrial Aerodynamics, vol. 152, pp. 41–52, 2016.

H. Zhou, J. Liu, C. Gao, W. Li, S. Ou, Y. Zhou, and Q. Luan, “Copula-based joint impact assessment of rainfall and tidal level on flood risk in tidal-influenced plain river network areas, Taihu Lake Basin,” Journal of Hydrology, vol. 653, p. 132785, 2025.

M. Khajehali, A. H. Bagherzadeh, A. R. Karami, and M. Nikoo, “A copula-based multivariate flood frequency analysis,” Scientific Reports, vol. 15, p. 84543, 2025.

C. Yu, D. Wang, V. P. Singh, P. Xu, A. Zhang, Z. Yang, Z. Wang, X. Zeng, J. Jiang, and J. Wu, “An ensemble vine copula quantile regression model with non-stationary margins (EVQR-NS) for soil moisture prediction,” Journal of Hydrology, vol. 659, p. 133248, 2025.

J. Sung, J. Kim, S. Kim, and Y.-S. Jung, “Comparative study of low flow frequency analysis using bivariate copula models,” Water, vol. 11, no. 6, p. 79, 2024.

M.-Z. Lyu, Z.-J. Fei, and D.-C. Feng, “Copula-based cloud analysis for seismic fragility and its application to nuclear power plant structures,” Engineering Structures, vol. 305, p. 117754, 2024.

D. A. Alexandre, C. Chaudhuri, and J. Gill-Fortin, “Novel extensions to the Fisher copula to model flood spatial dependence over North America,” Hydrology and Earth System Sciences, vol. 28, pp. 5069–5085, 2024.

E. De Amo, B. González, J. Martínez-Moreno, and A. Ortega, “Directional dependence orders of random vectors,” Mathematics, vol. 12, no. 3, p. 419, 2024.

R. B. Nelsen, An Introduction to Copulas, 2nd ed., Springer, 2006.

R Core Team, R: A Language and Environment for Statistical Computing, Vienna, 2025.

A. J. McNeil, R. Frey, and P. Embrechts, Quantitative Risk Management, Princeton University Press, 2005.

C. Genest, B. Rémillard, A. C. Favre, and J. Nešlehová, “Copula modeling from Abe Sklar to the present day,” Science of Probability and Statistics, 2024.

P. Jaworski, F. Durante, W. K. Hardle, and T. Rychlik, Copula Theory and Its Applications, Springer, 2010.

H. H. Ahmad, S. A. Bantan, M. Elgarhy, and A. H. Hendi, “Copula-linked modified Fréchet–exponential distributions: Maximum likelihood and IFM methods with real-world applications,” Mathematics, vol. 14, no. 6, p. 431, 2025.

E. M. Almetwally, A. H. Abd El‐Ghaly, M. S. Eliwa, and M. M. Mohie El‐Din, “Advanced copula-based models for type II censored data,” Mathematics, vol. 12, no. 12, p. 1774, 2024.

M. Mohammadi, M. A. Amini, and M. Emadi, “A simulation study of semiparametric estimation in copula models based on minimum alpha-divergence,” Computational Statistics, vol. 36, no. 2, pp. 613–640, 2021.

K. Otieno, L. Chaba, E. Omondi, and B. Omolo, “A hierarchical Archimedean copula model for climatic variables: An application to Kenyan data,” Frontiers in Applied Mathematics and Statistics, vol. 11, 2025.

S. Shrivastava, S. Gairola, A. K. Lohani, and A. Kumar, “Copula-based dependency modelling of hydraulic extremes in precipitation data,” Journal of Hydrology, vol. 603, p. 127006, 2025.

X. W. Zheng, H. K. Lam, Y. Xu, J. Li, and Y. Li, “Hybrid Bayesian-copula-based damage probability of tall buildings under concurrent seismic and strong wind,” Engineering Structures, vol. 300, p. 116753, 2024.

X. S. Tang, Y. F. Liu, W. Zhang, and S. C. Li, “Impact of copula selection on geotechnical reliability under incomplete probability information,” Geotechnical Reliability Engineering, vol. 43, pp. 57–70, 2013.

J. Rózsás, “The effect of copulas on time-variant reliability involving time-continuous stochastic processes,” Structural Safety, vol. 32, no. 4, pp. 335–345, 2010.

D. Meyer, T. Nagler, T. F. Münch, and S. N. Murphy, “Copula-based synthetic data augmentation for machine-learning emulators,” Geoscientific Model Development, vol. 14, pp. 5205–5222, 2021.

T. Nagler, C. Bumann, and C. Czado, “Model selection in sparse high-dimensional vine copula models…,” Journal of Multivariate Analysis, vol. 172, pp. 180–192, 2019.

D. H. Oh and A. J. Patton, “High-dimensional copula-based distributions with mixed frequency data,” Journal of Econometrics, vol. 193, no. 2, pp. 349–366, 2016.

Y. Hu and Y. Hou, “A copula-based approach to modelling and testing for heavy-tailed data with bivariate heteroscedastic extremes, arXiv e-prints, 2024.

H. Joe, Dependence Modeling with Copulas, CRC Press, 2015.

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Publicado

2026-04-06

Cómo citar

Popayán Hernández, J. G. (2026). Directional Dependence via Copulas: Examples and Applications in Engineering. Scientia Et Technica, 31(01), 21–31. https://doi.org/10.22517/23447214.25954

Número

Sección

Industrial