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Empirical Bayes Age–Period–Cohort Analysis of Retrospective Incidence Data

Author

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  • Yosihiko Ogata
  • Koichi Katsura
  • Niels Keiding
  • Claus Holst
  • Anders Green

Abstract

We analyse the (age, time)‐specific incidence of diabetes based on retrospective data obtained from a prevalent cohort only including survivors to a particular date. From underlying point processes with intensities corresponding to the (age, time)‐specific incidence rates the observed point pattern is assumed to be generated by an independent thinning process with parameters (assumed known) depending on population density and survival probability to the sampling date. A Bayesian procedure is carried out for the optimal adjustment and comparison of isotropic and anisotropic smoothing priors for the intensity functions, as well as for the decomposition of the intensity on the (time, age) Lexis diagram into the three factors of age, period and cohort.

Suggested Citation

  • Yosihiko Ogata & Koichi Katsura & Niels Keiding & Claus Holst & Anders Green, 2000. "Empirical Bayes Age–Period–Cohort Analysis of Retrospective Incidence Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 27(3), pages 415-432, September.
    • Handle: RePEc:bla:scjsta:v:27:y:2000:i:3:p:415-432
    DOI: 10.1111/1467-9469.00198
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    Citations

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    Cited by:

    1. Kosei Fukuda, 2006. "A cohort analysis of female labor participation rates in the U.S. and Japan," Review of Economics of the Household, Springer, vol. 4(4), pages 379-393, December.
    2. Kosei Fukuda, 2007. "Age-period-cohort decomposition of social security taxes and benefits in the USA and Japan," International Economics and Economic Policy, Springer, vol. 4(3), pages 227-240, November.
    3. Carl Schmertmann & Emilio Zagheni & Joshua R. Goldstein & Mikko Myrskylä, 2014. "Bayesian Forecasting of Cohort Fertility," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(506), pages 500-513, June.
    4. Yosihiko Ogata & Koichi Katsura & Masaharu Tanemura, 2003. "Modelling heterogeneous space–time occurrences of earthquakes and its residual analysis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 52(4), pages 499-509, October.
    5. Kosei Fukuda, 2006. "Age-period-cohort decomposition of aggregate data: an application to US and Japanese household saving rates," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(7), pages 981-998.
    6. Kosei Fukuda, 2006. "Age–period–cohort decomposition of aggregate data: an application to US and Japanese household saving rates," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(7), pages 981-998, November.
    7. Kosei Fukuda, 2007. "An empirical analysis of US and Japanese health insurance using age–period–cohort decomposition," Health Economics, John Wiley & Sons, Ltd., vol. 16(5), pages 475-489, May.
    8. Kosei Fukuda, 2008. "Age–Period–Cohort Decomposition of U.S. and Japanese Birth Rates," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 27(4), pages 385-402, August.
    9. Fukuda, Kosei, 2008. "Empirical evidence on intergenerational inequality of tax burdens in the U.S. and Japan," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 37(6), pages 2214-2220, December.
    10. Carlo Giovanni Camarda, 2019. "Smooth constrained mortality forecasting," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 41(38), pages 1091-1130.

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