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Goodness of fit in models for mortality data

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  • Camarda, Carlo Giovanni
  • Durbán, María

Abstract

Mortality data on an aggregate level are characterized by very large sample sizes. For this reason, uninformative outcomes are evident in common Goodness-of-Fit measures. In this paper we propose a new measure that allows comparison of different mortality models even for large sample sizes. Particularly, we develop a measure which uses a null model specifically designed for mortality data. Several simulation studies and actual applications will demonstrate the performances of this new measure with special emphasis on demographic models and Pspline approach.

Suggested Citation

  • Camarda, Carlo Giovanni & Durbán, María, 2008. "Goodness of fit in models for mortality data," DES - Working Papers. Statistics and Econometrics. WS ws083909, Universidad Carlos III de Madrid. Departamento de Estadística.
  • Handle: RePEc:cte:wsrepe:ws083909
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    References listed on IDEAS

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    1. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521785167.
    2. I. D. Currie & M. Durban & P. H. C. Eilers, 2006. "Generalized linear array models with applications to multidimensional smoothing," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(2), pages 259-280, April.
    3. Cameron, A Colin & Windmeijer, Frank A G, 1996. "R-Squared Measures for Count Data Regression Models with Applications to Health-Care Utilization," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(2), pages 209-220, April.
    4. Veall, Michael R & Zimmermann, Klaus F, 1996. "Pseudo-R-[superscript 2] Measures for Some Common Limited Dependent Variable Models," Journal of Economic Surveys, Wiley Blackwell, vol. 10(3), pages 241-259, September.
    5. Arũnas P. Verbyla & Brian R. Cullis & Michael G. Kenward & Sue J. Welham, 1999. "The Analysis of Designed Experiments and Longitudinal Data by Using Smoothing Splines," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 48(3), pages 269-311.
    6. Colin Cameron, A. & Windmeijer, Frank A. G., 1997. "An R-squared measure of goodness of fit for some common nonlinear regression models," Journal of Econometrics, Elsevier, vol. 77(2), pages 329-342, April.
    7. Mittlbock, M. & Waldhor, T., 2000. "Adjustments for R2-measures for Poisson regression models," Computational Statistics & Data Analysis, Elsevier, vol. 34(4), pages 461-472, October.
    8. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521780506.
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    Goodness of fit;

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