Skip to main content

Advertisement

Log in

Longevity and concentration in survival times: the log-scale-location family of failure time models

  • Published:
Lifetime Data Analysis Aims and scope Submit manuscript

Abstract

Evidence suggests that the increasing life expectancy levels at birth witnessed over the past centuries are associated with a decreasing concentration of the survival times. The purpose of this work is to study the relationships that exist between longevity and concentration measures for some regression models for the evolution of survival. In particular, we study a family of survival models that can be used to capture the observed trends in longevity and concentration over time. The parametric family of log-scale-location models is shown to allow for modeling different trends of expected value and concentration of survival times. An extension towards mixture models is also described in order to take into account scenarios where a fraction of the population experiences short term survival. Some results are also presented for such framework. The use of both the log-scale-location family and the mixture model is illustrated through an application to period life tables from the Human Mortality Database.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Arias E (2010) United States life tables, 2006. National vital statistics reports, vol 58(21). National Center for Health Statistics, Hyattsville

    Google Scholar 

  • Atkinson T (1970) On the measurement of inequality. J Econ Theory 2:244–263

    Article  MathSciNet  Google Scholar 

  • Baudisch A (2011) The pace and shape of ageing. Methods Ecol Evol 2(4):375–382

    Article  Google Scholar 

  • Bhattacharya N, Mahalanobis B (1967) Regional disparities in household consumption in India. J Am Stat Assoc 62(317):143–161

  • Bonetti M, Gigliarano C, Muliere P (2009) The Gini concentration test for survival data. Lifetime Data Anal 15:493–518

    Article  MathSciNet  MATH  Google Scholar 

  • Bongaarts J, Feeney G (2002) How long do we live? Popul Dev Rev 28(1):13–29

    Article  Google Scholar 

  • Booth H, Tickle L (2008) Mortality modelling and forecasting: a review of models. Ann Actuar Sci 3(I/II):3–43

  • Brown DC, Hayward MD, Montez JK, Humme RA, Chiu C, Hidajat MM (2012) The significance of education for mortality compression in the United States. Demography 49:819–840

    Article  Google Scholar 

  • Canudas-Romo V (2008) The modal age at death and the shifting mortality hypothesis. Demogr Res 19(30):1179–1204

    Article  Google Scholar 

  • Congdon P (2004) Modelling trends and inequality in small area mortality. J Appl Stat 31(6):603–622

    Article  MathSciNet  Google Scholar 

  • Cox DR (1972) Regression models and life-tables (with discussion). J R Stat Soc Ser B 34:187–220

    MathSciNet  MATH  Google Scholar 

  • Debón A, Martínez-Ruiz F, Montes F (2012) Temporal evolution of mortality indicators: application to spanish data. N Am Actuar J 16(3):364–377

    Article  Google Scholar 

  • Finkelstein MS (2003) A model of aging and a shape of the observed force of mortality. Lifetime Data Anal 9:93–109

    Article  MathSciNet  MATH  Google Scholar 

  • Fries JF (1980) Aging, natural death, and the compression of morbidity. N Engl J Med 303(3):130–135

    Article  Google Scholar 

  • Gastwirth JL (1971) A general definition of the Lorenz Curve. Econometrica 31:1037–1039

    Article  MATH  Google Scholar 

  • Gillespie DOS, Trotter MV, Tuljapurkar SD (2014) Divergence in age patterns of mortality change drives international divergence in lifespan inequality. Demography 51:1003–1017

    Article  Google Scholar 

  • Gini C (1912) Variabilità e mutabilità. Contributo allo studio delle distribuzioni e relazioni statistiche. Studi Economico-Giuridici dell’Università di Cagliari. III

  • Gini C (1914) Sulla misura della concentrazione e della variabilità dei caratteri. Atti R Ist Veneto Sci Lett Arti LXXIII(part 2):1203–1248

    Google Scholar 

  • Haberman S, Renshaw A (2008) Mortality, longevity and experiments with the LeeCarter model. Lifetime Data Anal 14:286–315

    Article  MathSciNet  MATH  Google Scholar 

  • Hanada K (1983) A formula of Gini’s concentration ratio and its applications to life tables. J Jpn Stat Soc 19:293–325

    MATH  Google Scholar 

  • Human Mortality Database (2015) University of California, Berkeley (USA), and Max Planck Institute for Demographic Research (Germany). http://www.mortality.org

  • Johnson NL, Kotz S, Balakrishnan N (1995) Continuous univariate distributions, vol 2. Wiley, Hoboken

    MATH  Google Scholar 

  • Kakwani NC (1980) Income inequality and poverty: methods of estimation and policy applications. Oxford University Press, Oxford

    Google Scholar 

  • Kannisto V (2000) Measuring the compression of mortality. Demogr Res 3(6):1–24

    Google Scholar 

  • Kannisto V (2001) Mode and dispersion of length of life. Population 13(1):159–172

    Google Scholar 

  • Kendall M, Stuart A (1977) The advanced theory of statistics, vol I. Mac Millan Publishing, New York

    MATH  Google Scholar 

  • Lawless JF (2003) Statistical models and methods for lifetime data. Wiley, New York

    MATH  Google Scholar 

  • Lorenz MO (1905) Methods of measuring the concentration of wealth. Publ Am Stat Assoc 9(70):209–219

    Google Scholar 

  • Michetti B, Dall’Aglio G (1957) La differenza semplice media. Statistica 7(2):159–255

    MathSciNet  Google Scholar 

  • Muliere P, Scarsini M (1989) A note on stochastic dominance and inequality measures. J Econ Theory 49:314–323

    Article  MathSciNet  MATH  Google Scholar 

  • Nygard F, Sandröm A (1981) Measuring income inequality. Almqvist and Wilsell International, Stockholm

    Google Scholar 

  • Oeppen J, Vaupel JW (2002) Broken limits to life expectancy. Science 296:1029–1031

    Article  Google Scholar 

  • Ostasiewicz K, Mazurek E (2013) Comparison of the Gini and Zenga indexes using some theoretical income distributions abstract. Oper Res Decis 1:37–62

    MathSciNet  Google Scholar 

  • Pietra G (1915) Delle relazioni tra gli indici di variabilità I, II. Atti R Ist Veneto Sci Lett Arti LXXIV(II):775–804

    Google Scholar 

  • Shaked M, Shanthikumar JG (1994) Stochastic orders and their applications. Academic Press Inc, Boston

    MATH  Google Scholar 

  • Shaked M, Shanthikumar JG (2007) Stochastic orders. Springer, New york

  • Shkolnikov VM, Andreev EE, Begun AZ (2003) Gini coefficient as a life table function: computation from discrete data, decomposition of differences and empirical examples. Demogr Res 8:305–358

    Article  Google Scholar 

  • United Nations, Department of Economic and Social Affairs, Population Division (2013) World population ageing. ST/ESA/SER.A/348

  • van Raalte AA, Martikainen P, Myrskylä M (2014) Lifespan variation by occupational class: compression or stagnation over time? Demography 51:73–95

    Article  Google Scholar 

  • Vaupel JW, Canudas-Romo V (2003) Decomposing change in life expectancy: a bouquet of formulas in honor of Nathan Keyfitz’s 90th birthday. Demography 40:201–216

    Article  Google Scholar 

  • Wilmoth JR, Horiuchi S (1999) Rectangularization revisited: variability in age at death within human populations. Demography 36(4):475–95

    Article  Google Scholar 

  • Wrycza TF, Baudisch A (2014) The pace of aging: intrinsic time scales in demography. Demogr Res 30(57):1571–1590

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Marco Bonetti.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 101 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gigliarano, C., Basellini, U. & Bonetti, M. Longevity and concentration in survival times: the log-scale-location family of failure time models. Lifetime Data Anal 23, 254–274 (2017). https://doi.org/10.1007/s10985-016-9356-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10985-016-9356-1

Keywords

Navigation