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A Comparison of Nonparametric Methods in the Graduation of Mortality: Application to Data from the Valencia Region (Spain)

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A Comparison of Nonparametric Methods in the Graduation of Mortality: Application to Data from the Valencia Region (Spain)

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Debón Aucejo, AM.; Montes-Suay, F.; Sala-Garrido, R. (2006). A Comparison of Nonparametric Methods in the Graduation of Mortality: Application to Data from the Valencia Region (Spain). International Statistical Review. 74(2):215-233. https://doi.org/10.1111/j.1751-5823.2006.tb00171.x

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Metadatos del ítem

Título: A Comparison of Nonparametric Methods in the Graduation of Mortality: Application to Data from the Valencia Region (Spain)
Autor: Debón Aucejo, Ana María Montes-Suay, Francisco Sala-Garrido, Ramón
Entidad UPV: Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat
Fecha difusión:
Resumen:
[EN] The nonparametric graduation of mortality data aims to estimate death rates by carrying out a smoothing of the crude rates obtained directly from original data. The main difference with regard to parametric models is ...[+]
Palabras clave: GAM , Kernel smoothing , Life tables , LOESS , Splines
Derechos de uso: Reserva de todos los derechos
Fuente:
International Statistical Review. (issn: 0306-7734 )
DOI: 10.1111/j.1751-5823.2006.tb00171.x
Editorial:
Blackwell Publishing
Versión del editor: https://doi.org/10.1111/j.1751-5823.2006.tb00171.x
Código del Proyecto:
info:eu-repo/grantAgreement/GVA//GRUPOS03%2F189/
info:eu-repo/grantAgreement/MEC//MTM2004-06231/ES/MODELIZACION ESTADISTICA PARA DATOS CON IMPLANTACION ESPACIAL Y EVOLUCION TEMPORAL. APLICACIONES EN TABLAS DINAMICAS DE MORTALIDAD Y POTENCIALES EVOCADOS EN PSICOLOGIA Y NEUROFISIOLOGIA./
Agradecimientos:
The authors are indebted to the anonymous referees whose suggestions improved the original manuscript. This work was partially supported by a grant from MEyC (Ministerio de Educación y Ciencia, Spain, project MTM-2004-06231).The ...[+]
Tipo: Artículo

References

Bloomfield, D. S. F., & Haberman, S. (1987). Graduation: some experiments with kernel methods. Journal of the Institute of Actuaries, 114(2), 339-369. doi:10.1017/s0020268100019090

Breiman, L., & Peters, S. (1992). Comparing Automatic Smoothers (A Public Service Enterprise). International Statistical Review / Revue Internationale de Statistique, 60(3), 271. doi:10.2307/1403679

Brockmann, M., Gasser, T., & Herrmann, E. (1993). Locally Adaptive Bandwidth Choice for Kernel Regression Estimators. Journal of the American Statistical Association, 88(424), 1302-1309. doi:10.1080/01621459.1993.10476411 [+]
Bloomfield, D. S. F., & Haberman, S. (1987). Graduation: some experiments with kernel methods. Journal of the Institute of Actuaries, 114(2), 339-369. doi:10.1017/s0020268100019090

Breiman, L., & Peters, S. (1992). Comparing Automatic Smoothers (A Public Service Enterprise). International Statistical Review / Revue Internationale de Statistique, 60(3), 271. doi:10.2307/1403679

Brockmann, M., Gasser, T., & Herrmann, E. (1993). Locally Adaptive Bandwidth Choice for Kernel Regression Estimators. Journal of the American Statistical Association, 88(424), 1302-1309. doi:10.1080/01621459.1993.10476411

Chan,, F. Y., Chan, L. K., Falkenberg, J., & Yu, M. H. (1986). Applications of linear and quadratic programmings to some cases of the Whittaker-Henderson graduation method. Scandinavian Actuarial Journal, 1986(3-4), 141-153. doi:10.1080/03461238.1986.10413805

Clements, M. S. (2005). Lung cancer rate predictions using generalized additive models. Biostatistics, 6(4), 576-589. doi:10.1093/biostatistics/kxi028

Cleveland, W. S. (1979). Robust Locally Weighted Regression and Smoothing Scatterplots. Journal of the American Statistical Association, 74(368), 829-836. doi:10.1080/01621459.1979.10481038

Copas, J. B., & Haberman, S. (1983). Non-parametric graduation using kernel methods. Journal of the Institute of Actuaries, 110(01), 135-156. doi:10.1017/s0020268100041275

Epanechnikov, V. A. (1969). Non-Parametric Estimation of a Multivariate Probability Density. Theory of Probability & Its Applications, 14(1), 153-158. doi:10.1137/1114019

Felipe, A., Guillen, M., & Perez-Marin, A. M. (2002). Recent Mortality Trends in the Spanish Population. British Actuarial Journal, 8(4), 757-786. doi:10.1017/s1357321700003901

Forfar, D. O., McCutcheon, J. J., & Wilkie, A. D. (1988). On graduation by mathematical formula. Journal of the Institute of Actuaries, 115(1), 1-149. doi:10.1017/s0020268100042633

Gasser, T., Kneip, A., & Köhler, W. (1991). A Flexible and Fast Method for Automatic Smoothing. Journal of the American Statistical Association, 86(415), 643-652. doi:10.1080/01621459.1991.10475090

Gasser, T., & Müller, H.-G. (1979). Kernel estimation of regression functions. Smoothing Techniques for Curve Estimation, 23-68. doi:10.1007/bfb0098489

Gavin, J. B., Haberman, S., & Verrall, R. J. (1994). On the choice of bandwidth for kernel graduation. Journal of the Institute of Actuaries, 121(1), 119-134. doi:10.1017/s0020268100020102

Guillen, M., Nielsen, J. P., & Perez-Marin, A. M. (2006). Multiplicative Hazard Models for Studying the Evolution of Mortality. Annals of Actuarial Science, 1(1), 165-177. doi:10.1017/s1748499500000099

Heligman, L., & Pollard, J. H. (1980). The age pattern of mortality. Journal of the Institute of Actuaries, 107(1), 49-80. doi:10.1017/s0020268100040257

Hoem, J. M., & Linnemann, P. (1988). The tails in moving average graduation. Scandinavian Actuarial Journal, 1988(4), 193-229. doi:10.1080/03461238.1988.10413848

McCullagh, P., & Nelder, J. A. (1989). Generalized Linear Models. doi:10.1007/978-1-4899-3242-6

Nadaraya, E. A. (1964). On Estimating Regression. Theory of Probability & Its Applications, 9(1), 141-142. doi:10.1137/1109020

Nielsen, J. P. (2003). Smoothing and Prediction with a View to Actuarial Science, Biostatistics and Finance. Scandinavian Actuarial Journal, 2003(1), 51-74. doi:10.1080/03461230308484

Silverman, B. W. (1984). A Fast and Efficient Cross-Validation Method for Smoothing Parameter Choice in Spline Regression. Journal of the American Statistical Association, 79(387), 584-589. doi:10.1080/01621459.1984.10478084

Verrall, R. J. (1993). Graduation by dynamic regression methods. Journal of the Institute of Actuaries, 120(1), 153-170. doi:10.1017/s002026810003688x

Wang, J.-L., Müller, H.-G., & Capra, W. B. (1998). Analysis of oldest-old mortality: lifetables revisited. The Annals of Statistics, 26(1), 126-163. doi:10.1214/aos/1030563980

Whittaker, E. T. (1922). On a New Method of Graduation. Proceedings of the Edinburgh Mathematical Society, 41, 63-75. doi:10.1017/s0013091500077853

Zhang, H. (2004). Mixed effects multivariate adaptive splines model for the analysis of longitudinal and growth curve data. Statistical Methods in Medical Research, 13(1), 63-82. doi:10.1191/0962280204sm353ra

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