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dc.contributor.author | Diaz-Rojo, Gisou![]() |
es_ES |
dc.contributor.author | Debón Aucejo, Ana María![]() |
es_ES |
dc.contributor.author | Mosquera, Jaime![]() |
es_ES |
dc.date.accessioned | 2021-02-24T04:32:17Z | |
dc.date.available | 2021-02-24T04:32:17Z | |
dc.date.issued | 2020-11 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/162259 | |
dc.description.abstract | [EN] The mortality structure of a population usually reflects the economic and social development of the country. The purpose of this study was to identify moments in time and age intervals at which the observed probability of death is substantially different from the pattern of mortality for a studied period. Therefore, a mortality model was fitted to decompose the historical pattern of mortality. The model residuals were monitored by the T-2 multivariate control chart to detect substantial changes in mortality that were not identified by the model. The abridged life tables for Colombia in the period 1973-2005 were used as a case study. The Lee-Carter model collects information regarding violence in Colombia. Therefore, the years identified as out-of-control in the charts are associated with very early or quite advanced ages of death and are inversely related to the violence that did not claim as many victims at those ages. The mortality changes identified in the control charts pertain to changes in the population's health conditions or new causes of death such as COVID-19 in the coming years. The proposed methodology is generalizable to other countries, especially developing countries. | es_ES |
dc.description.sponsorship | This research received external funding from the Universitat Politecnica de Valencia (UPV) and the Universidad del Tolima (UT) to cover translation and publication costs. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | MDPI AG | es_ES |
dc.relation.ispartof | Mathematics | es_ES |
dc.rights | Reconocimiento (by) | es_ES |
dc.subject | Lee-Carter model | es_ES |
dc.subject | Life table | es_ES |
dc.subject | Multivariate control charts | es_ES |
dc.subject | T2 control chart | es_ES |
dc.subject | MTY decomposition | es_ES |
dc.subject.classification | ESTADISTICA E INVESTIGACION OPERATIVA | es_ES |
dc.title | Multivariate Control Chart and Lee-Carter Models to Study Mortality Changes | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.3390/math8112093 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MINECO//MTM2013-45381-P/ES/DIFERENCIAS DE LONGEVIDAD EN LA UNION EUROPEA: APLICACION DE NUEVOS METODOS PARA SU EVALUACION Y ANALISIS/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat | es_ES |
dc.description.bibliographicCitation | Diaz-Rojo, G.; Debón Aucejo, AM.; Mosquera, J. (2020). Multivariate Control Chart and Lee-Carter Models to Study Mortality Changes. Mathematics. 8(11):1-17. https://doi.org/10.3390/math8112093 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.3390/math8112093 | es_ES |
dc.description.upvformatpinicio | 1 | es_ES |
dc.description.upvformatpfin | 17 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 8 | es_ES |
dc.description.issue | 11 | es_ES |
dc.identifier.eissn | 2227-7390 | es_ES |
dc.relation.pasarela | S\422496 | es_ES |
dc.contributor.funder | Universidad del Tolima | es_ES |
dc.contributor.funder | Universitat Politècnica de València | es_ES |
dc.contributor.funder | Ministerio de Economía y Competitividad | es_ES |
dc.description.references | Alexopoulos, A., Dellaportas, P., & Forster, J. J. (2018). Bayesian forecasting of mortality rates by using latent Gaussian models. Journal of the Royal Statistical Society: Series A (Statistics in Society), 182(2), 689-711. doi:10.1111/rssa.12422 | es_ES |
dc.description.references | Callot, L., Haldrup, N., & Kallestrup-Lamb, M. (2015). Deterministic and stochastic trends in the Lee–Carter mortality model. Applied Economics Letters, 23(7), 486-493. doi:10.1080/13504851.2015.1083075 | es_ES |
dc.description.references | Carfora, M. F., Cutillo, L., & Orlando, A. (2017). A quantitative comparison of stochastic mortality models on Italian population data. Computational Statistics & Data Analysis, 112, 198-214. doi:10.1016/j.csda.2017.03.012 | es_ES |
dc.description.references | Booth, H., Hyndman, R. J., Tickle, L., & de Jong, P. (2006). Lee-Carter mortality forecasting: a multi-country comparison of variants and extensions. Demographic Research, 15, 289-310. doi:10.4054/demres.2006.15.9 | es_ES |
dc.description.references | Salhi, Y., & Loisel, S. (2016). Basis risk modelling: a cointegration-based approach. Statistics, 51(1), 205-221. doi:10.1080/02331888.2016.1259806 | es_ES |
dc.description.references | Postigo-Boix, M., Agüero, R., & Melús-Moreno, J. L. (2019). An alternative procedure to obtain the mortality rate with non-linear functions: Application to the case of the Spanish population. PLOS ONE, 14(10), e0223789. doi:10.1371/journal.pone.0223789 | es_ES |
dc.description.references | Belliard, M., & Williams, I. (2013). Proyección estocástica de la mortalidad. Una aplicación de Lee-Carter en la Argentina. Revista Latinoamericana de Población, 7(13), 129-148. doi:10.31406/relap2013.v7.i2.n13.6 | es_ES |
dc.description.references | García Guerrero, V. M., & Ordorica Mellado, M. (2012). Proyección estocástica de la mortalidad mexicana por medio del método de Lee-Carter / Stochastic Projection of Mexican Mortality through the Lee-Carter Method. Estudios Demográficos y Urbanos, 27(2), 409. doi:10.24201/edu.v27i2.1418 | es_ES |
dc.description.references | Diaz, G., Debón, A., & Giner-Bosch, V. (2018). Mortality forecasting in Colombia from abridged life tables by sex. Genus, 74(1). doi:10.1186/s41118-018-0038-6 | es_ES |
dc.description.references | Mason, R. L., Tracy, N. D., & Young, J. C. (1995). Decomposition ofT2 for Multivariate Control Chart Interpretation. Journal of Quality Technology, 27(2), 99-108. doi:10.1080/00224065.1995.11979573 | es_ES |
dc.description.references | Shewhart, W. A. (1927). Quality Control. Bell System Technical Journal, 6(4), 722-735. doi:10.1002/j.1538-7305.1927.tb00215.x | es_ES |
dc.description.references | Woodall, W. H. (2006). The Use of Control Charts in Health-Care and Public-Health Surveillance. Journal of Quality Technology, 38(2), 89-104. doi:10.1080/00224065.2006.11918593 | es_ES |
dc.description.references | Vetter, T. R., & Morrice, D. (2019). Statistical Process Control. Anesthesia & Analgesia, 128(2), 374-382. doi:10.1213/ane.0000000000003977 | es_ES |
dc.description.references | Benneyan, J. C. (2003). Statistical process control as a tool for research and healthcare improvement. Quality and Safety in Health Care, 12(6), 458-464. doi:10.1136/qhc.12.6.458 | es_ES |
dc.description.references | Imam, N., Spelman, T., Johnson, S. A., & Worth, L. J. (2019). Statistical Process Control Charts for Monitoring Staphylococcus aureus Bloodstream Infections in Australian Health Care Facilities. Quality Management in Health Care, 28(1), 39-44. doi:10.1097/qmh.0000000000000201 | es_ES |
dc.description.references | Williamson, G. D., & Weatherby Hudson, G. (1999). A monitoring system for detecting aberrations in public health surveillance reports. Statistics in Medicine, 18(23), 3283-3298. doi:10.1002/(sici)1097-0258(19991215)18:23<3283::aid-sim316>3.0.co;2-z | es_ES |
dc.description.references | Thacker, S. B., Stroup, D. F., Rothenberg, R. B., & Brownson, R. C. (1995). Public health surveillance for chronic conditions: A scientific basis for decisions. Statistics in Medicine, 14(5-7), 629-641. doi:10.1002/sim.4780140520 | es_ES |
dc.description.references | Yue, J., Lai, X., Liu, L., & Lai, P. B. S. (2017). A new VLAD-based control chart for detecting surgical outcomes. Statistics in Medicine, 36(28), 4540-4547. doi:10.1002/sim.7362 | es_ES |
dc.description.references | Chamberlin, W. H., Lane, K. A., Kennedy, J. N., Bradley, S. D., & Rice, C. L. (1993). Monitoring intensive care unit performance using statistical quality control charts. International Journal of Clinical Monitoring and Computing, 10(3), 155-161. doi:10.1007/bf01246449 | es_ES |
dc.description.references | Marshall, T., & Mohammed, M. A. (2007). Case-mix and the use of control charts in monitoring mortality rates after coronary artery bypass. BMC Health Services Research, 7(1). doi:10.1186/1472-6963-7-63 | es_ES |
dc.description.references | Briceno-Leon, R., Villaveces, A., & Concha-Eastman, A. (2008). Understanding the uneven distribution of the incidence of homicide in Latin America. International Journal of Epidemiology, 37(4), 751-757. doi:10.1093/ije/dyn153 | es_ES |
dc.description.references | Gaviria, A. (2000). Increasing returns and the evolution of violent crime: the case of Colombia. Journal of Development Economics, 61(1), 1-25. doi:10.1016/s0304-3878(99)00059-0 | es_ES |
dc.description.references | Latin American Human Mortality Databasewww.lamortalidad.org | es_ES |
dc.description.references | BOOTH, H., MAINDONALD, J., & SMITH, L. (2002). Applying Lee-Carter under conditions of variable mortality decline. Population Studies, 56(3), 325-336. doi:10.1080/00324720215935 | es_ES |
dc.description.references | Renshaw, A. E., & Haberman, S. (2003). Lee–Carter mortality forecasting with age-specific enhancement. Insurance: Mathematics and Economics, 33(2), 255-272. doi:10.1016/s0167-6687(03)00138-0 | es_ES |
dc.description.references | Debón, A., Montes, F., & Puig, F. (2008). Modelling and forecasting mortality in Spain. European Journal of Operational Research, 189(3), 624-637. doi:10.1016/j.ejor.2006.07.050 | es_ES |
dc.description.references | Debón, A., Martínez-Ruiz, F., & Montes, F. (2010). A geostatistical approach for dynamic life tables: The effect of mortality on remaining lifetime and annuities. Insurance: Mathematics and Economics, 47(3), 327-336. doi:10.1016/j.insmatheco.2010.07.007 | es_ES |
dc.description.references | Wang, D., & Lu, P. (2005). Modelling and forecasting mortality distributions in England and Wales using the Lee–Carter model. Journal of Applied Statistics, 32(9), 873-885. doi:10.1080/02664760500163441 | es_ES |
dc.description.references | Renshaw, A. E., & Haberman, S. (2008). On simulation-based approaches to risk measurement in mortality with specific reference to Poisson Lee–Carter modelling. Insurance: Mathematics and Economics, 42(2), 797-816. doi:10.1016/j.insmatheco.2007.08.009 | es_ES |
dc.description.references | Coelho, E., & Nunes, L. C. (2011). Forecasting mortality in the event of a structural change. Journal of the Royal Statistical Society: Series A (Statistics in Society), 174(3), 713-736. doi:10.1111/j.1467-985x.2010.00687.x | es_ES |
dc.description.references | Villegas, A. M., Kaishev, V. K., & Millossovich, P. (2018). StMoMo: An R Package for Stochastic Mortality Modeling. Journal of Statistical Software, 84(3). doi:10.18637/jss.v084.i03 | es_ES |
dc.description.references | Tracy, N. D., Young, J. C., & Mason, R. L. (1992). Multivariate Control Charts for Individual Observations. Journal of Quality Technology, 24(2), 88-95. doi:10.1080/00224065.1992.12015232 | es_ES |
dc.description.references | Champ, C. W., & Jones-Farmer, L. A. (2007). Properties of Multivariate Control Charts with Estimated Parameters. Sequential Analysis, 26(2), 153-169. doi:10.1080/07474940701247040 | es_ES |
dc.description.references | gnm: Generalized Nonlinear Models. R Package Version 1.0-8https://CRAN.R-project.org/package=gnm | es_ES |
dc.description.references | qcc: Quality Control Charting. R Package Version 2.7https://CRAN.R-project.org/package=qcc | es_ES |
dc.description.references | Urdinola, B. P., Torres Áviles†, F., & Velasco, J. A. (2017). The Homicide Atlas in Colombia: Contagion and Under-Registration for Small Areas. Cuadernos de Geografía: Revista Colombiana de Geografía, 26(1), 101-118. doi:10.15446/rcdg.v26n1.55429 | es_ES |
dc.subject.ods | 03.- Garantizar una vida saludable y promover el bienestar para todos y todas en todas las edades | es_ES |