Cluster-Specific Ranking and Variable Importance for Scottish Regional Deprivation via Vine Mixtures

Özge Şahin (), Ozan Evkaya and Ariane Hanebeck
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Özge Şahin: Delft University of Technology, Delft Institute of Applied Mathematics
Ozan Evkaya: University of Edinburgh, School of Mathematics and Maxwell Institute for Mathematical Sciences
Ariane Hanebeck: Technical University of Munich, Department of Mathematics, School of Computation, Information and Technology

A chapter in Statistical Dependence Modeling, 2026, pp 205-237 from Springer

Abstract: Abstract Socioeconomic deprivation is a key determinant of public health, as highlighted by the Scottish Government’s Scottish Index of Multiple Deprivation (SIMD). We propose an approach for clustering Scottish zones based on multiple deprivation indicators using vine mixture models. This framework uses the flexibility of vine copulas to capture tail dependent and asymmetric relationships among the indicators. From the fitted vine mixture model, we obtain posterior probabilities for each zone’s membership in clusters. This allows the construction of a cluster-driven deprivation ranking by sorting zones according to their probability of belonging to the most deprived cluster. To assess variable importance in this unsupervised learning setting, we adopt a leave-one-variable-out procedure by refitting the model without each variable and calculating the resulting change in the Bayesian information criterion. Our analysis of 21 continuous indicators across 1964 zones in Glasgow and the surrounding areas in Scotland shows that socioeconomic measures, particularly income and employment rates, are major drivers of deprivation, while certain health- and crime-related indicators appear less influential. These findings are consistent across the approach of variable importance and the analysis of the fitted vine structures of the identified clusters.

Keywords: Multiple deprivation; Clustering; Copula; Vine mixture; Variable importance (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1007/978-3-032-14252-8_10

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