Journal of Data Science and Modeling

A Mahalanobis-Distance-Based Copula Model Averaging Approach for Dependence Modelling with an Application to the Tehran Province Earthquake Catalogue

Articles in Press

Document Type : original

Authors

Sedigheh Shams
Mahnaz Rahmani

Department of Statistics, Faculty of Mathematical Sciences, Alzahra University

https://doi.org/10.22054/jdsm.2026.94057.1098
Abstract
Copula models provide a flexible framework for modelling complex dependence structures between continuous variables. However, several competing copula families may provide similarly satisfactory fits, making inference based on a single selected model potentially unstable. To address this issue, a Mahalanobis-distance-based copula model-averaging approach is adopted to account for model uncertainty and obtain more stable estimates of conditional exceedance probabilities. Its performance is evaluated through simulation studies across a range of Kendall's $\tau$ values and illustrated using an earthquake catalogue from Tehran Province, Iran, to examine dependence between earthquake magnitude and inter-event time. The simulation results demonstrate stable estimation across different dependence structures. In the real-data application, appropriate marginal models are fitted before applying the copula framework, and the estimated conditional exceedance probabilities vary only slightly across elapsed-time thresholds. These findings suggest that elapsed time alone provides limited predictive information about the magnitude of subsequent earthquakes and highlight the value of incorporating model uncertainty into copula-based dependence modelling.

Keywords

Copula model
Mahalanobis distance
Model averaging
Dependence modelling
Earthquake catalogue
Subjects
Journal of Data Science and Modeling

Articles in Press, Accepted Manuscript
Available Online from 02 October 2026

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