An algorithm for reconciling indicators across multiple dimensions: weighted iterative proportional fitting
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Jose M. Pavía
[1]
;
Josep Lledó
[1]
;
Priscila Espinosa
[1]
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[1] Universitat de València
Universitat de València
Valencia, España
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- Localización: Sort: Statistics and Operations Research Transactions, ISSN 1696-2281, Vol. 50, Nº. 1, 2026, págs. 67-88
- Idioma: inglés
- DOI: 10.57645/20.8080.02.32
- Enlaces
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Resumen
Reconciling multidimensional count data across multiple sources is a common challenge in social and economic research. Iterative proportional fitting is widely used for this purpose, but aligning indices under weighted sum-convex constraints calls for a more flexible approach. We introduce the weighted iterative proportional fitting algorithm, which incorporates sum-weighted constraints to adjust indicators—such as death-risk indices by wealth, habitat, and climate—while preserving marginal consistency. Weighted iterative proportional fitting has been implemented in an R package of the same name, enabling scholars, statisticians, and policymakers’ advisors, among others, to apply it easily to multidimensional data.
