A concordance coefficient for lattice data: An application to poverty indices in Chile
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915303862042624 |
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| author | Vallejos, Ronny Ferrer, Clemente Mateu, Jorge |
| author_facet | Vallejos, Ronny Ferrer, Clemente Mateu, Jorge |
| contents | This paper introduces a novel coefficient for measuring agreement between two lattice sequences observed in the same areal units, motivated by the analysis of different methodologies for measuring poverty rates in Chile. Building on the multivariate concordance coefficient framework, our approach accounts for dependencies in the multivariate lattice process using a non-negative definite matrix of weights, assuming a Multivariate Conditionally Autoregressive (GMCAR) process. We adopt a Bayesian perspective for inference, using summaries from Bayesian estimates. The methodology is illustrated through an analysis of poverty rates in the Metropolitan and Valparaíso regions of Chile, with High Posterior Density (HPD) intervals provided for the poverty rates. This work addresses a methodological gap in the understanding of agreement coefficients and enhances the usability of these measures in the context of social variables typically assessed in areal units. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_18935 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A concordance coefficient for lattice data: An application to poverty indices in Chile Vallejos, Ronny Ferrer, Clemente Mateu, Jorge Methodology Applications This paper introduces a novel coefficient for measuring agreement between two lattice sequences observed in the same areal units, motivated by the analysis of different methodologies for measuring poverty rates in Chile. Building on the multivariate concordance coefficient framework, our approach accounts for dependencies in the multivariate lattice process using a non-negative definite matrix of weights, assuming a Multivariate Conditionally Autoregressive (GMCAR) process. We adopt a Bayesian perspective for inference, using summaries from Bayesian estimates. The methodology is illustrated through an analysis of poverty rates in the Metropolitan and Valparaíso regions of Chile, with High Posterior Density (HPD) intervals provided for the poverty rates. This work addresses a methodological gap in the understanding of agreement coefficients and enhances the usability of these measures in the context of social variables typically assessed in areal units. |
| title | A concordance coefficient for lattice data: An application to poverty indices in Chile |
| topic | Methodology Applications |
| url | https://arxiv.org/abs/2505.18935 |