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On ordered subpopulations and population mortality at advanced ages

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  • Finkelstein, Maxim

Abstract

We consider hazard (mortality) rates in heterogeneous populations consisting of ordered (in the defined stochastic sense) subpopulations. This setting can be interpreted via the fixed frailty models with one or more frailty parameters. The shape of the hazard rate is of main interest in this paper. Specifically, the deceleration and leveling off in the hazard rates (mortality plateaus) are discussed and some examples of lifetime distributions that can result in asymptotically flat hazard rates are considered. These examples are based on vitality models when an organism’s initial vitality (resource) is ‘consumed’ in the course of life in accordance with a simple stochastic process (e.g., the Wiener process with drift or the gamma process).

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  • Finkelstein, Maxim, 2012. "On ordered subpopulations and population mortality at advanced ages," Theoretical Population Biology, Elsevier, vol. 81(4), pages 292-299.
    • Handle: RePEc:eee:thpobi:v:81:y:2012:i:4:p:292-299
    DOI: 10.1016/j.tpb.2012.01.005
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    References listed on IDEAS

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    1. repec:cai:popine:popu_p1976_31n3_0692 is not listed on IDEAS
    2. Maxim Finkelstein, 2009. "Understanding the shape of the mixture failure rate (with engineering and demographic applications)," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 25(6), pages 643-663, November.
    3. Maxim S. Finkelstein, 2009. "Understanding the shape of the mixture failure rate (with engineering and demographic applications)," MPIDR Working Papers WP-2009-031, Max Planck Institute for Demographic Research, Rostock, Germany.
    4. van Noortwijk, J.M., 2009. "A survey of the application of gamma processes in maintenance," Reliability Engineering and System Safety, Elsevier, vol. 94(1), pages 2-21.
    5. Missov, Trifon I. & Finkelstein, Maxim, 2011. "Admissible mixing distributions for a general class of mixture survival models with known asymptotics," Theoretical Population Biology, Elsevier, vol. 80(1), pages 64-70.
    6. David Steinsaltz & Kenneth Wachter, 2006. "Understanding Mortality Rate Deceleration and Heterogeneity," Mathematical Population Studies, Taylor & Francis Journals, vol. 13(1), pages 19-37.
    7. Li, Ting & Anderson, James J., 2009. "The vitality model: A way to understand population survival and demographic heterogeneity," Theoretical Population Biology, Elsevier, vol. 76(2), pages 118-131.
    8. Anatoli Yashin & Ivan Iachine & Alexander Begun, 2000. "Mortality modeling: A review," Mathematical Population Studies, Taylor & Francis Journals, vol. 8(4), pages 305-332.
    9. James Vaupel & Kenneth Manton & Eric Stallard, 1979. "The impact of heterogeneity in individual frailty on the dynamics of mortality," Demography, Springer;Population Association of America (PAA), vol. 16(3), pages 439-454, August.
    10. Liao, Haitao & Elsayed, Elsayed A. & Chan, Ling-Yau, 2006. "Maintenance of continuously monitored degrading systems," European Journal of Operational Research, Elsevier, vol. 175(2), pages 821-835, December.
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    Cited by:

    1. Cha, Ji Hwan & Finkelstein, Maxim, 2016. "Justifying the Gompertz curve of mortality via the generalized Polya process of shocks," Theoretical Population Biology, Elsevier, vol. 109(C), pages 54-62.
    2. Elizabeth Wrigley-Field, 2020. "Multidimensional Mortality Selection: Why Individual Dimensions of Frailty Don’t Act Like Frailty," Demography, Springer;Population Association of America (PAA), vol. 57(2), pages 747-777, April.
    3. Finkelstein, Maxim, 2013. "Lifesaving, delayed deaths and cure in mortality modeling," Theoretical Population Biology, Elsevier, vol. 83(C), pages 15-19.
    4. Cha, Ji Hwan & Finkelstein, Maxim, 2014. "Some notes on unobserved parameters (frailties) in reliability modeling," Reliability Engineering and System Safety, Elsevier, vol. 123(C), pages 99-103.

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