Hostname: page-component-cd9895bd7-lnqnp Total loading time: 0 Render date: 2024-12-27T23:54:48.014Z Has data issue: false hasContentIssue false
  • English
  • Français

Bias in Select Mortality Investigations

Published online by Cambridge University Press:  10 June 2011

R.G. Chadburn
R.G. Chadburn
Affiliation:
Actuarial Education Co, 31 Bath Street, Abingdon, Oxfordshire, OX14 3FF, U.K. Tel: +44 (0)1235 550005; Fax: +44 (0)1235 550085; E-mail: robert-chadburn@dial.pipex.com
Get access Rights & Permissions [Opens in a new window]

Abstract

The bias inherent in select mortality investigations where data are grouped by coincident or non-coincident rate intervals is analysed and compared. The key to the bias is shown to be the particular patterns of non-uniform ‘exposure frequency’ inherent in each method. It is shown that the bias using the non-coincident rate intervals is sensitive to a non-uniform distribution of new entrants by age, while the use of coincident rate intervals is sensitive to making appropriate assumptions regarding the distribution of policy anniversaries over life-time, p(s). Under both methods a significant proportion of the bias is retained after graduation. It is shown that non-coincident rate intervals may be preferred where p(s) is unknown, as in the CMI Bureau's investigations, while coincident rate intervals would be preferred where p(s) is known and taken into account in deriving the age for the estimated rates.

Keywords

EstimationExposure FrequencyMortalityRate Interval
Type
Sessional meetings: papers and abstracts of discussions
Information
British Actuarial Journal , Volume 2 , Issue 1 , 01 April 1996 , pp. 157 - 183
Copyright
Copyright © Institute and Faculty of Actuaries 1996

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Chadburn, R.G. (1991). Bias in select mortality investigations where data are subdivided by age at death. City University Actuarial Research Paper No. 31. (Institute of Actuaries Library, Oxford.)Google Scholar
Chadburn, R.G. (1993). Bias in select mortality investigations. City University Actuarial Research Paper No. 56. (Institute of Actuaries Library, Oxford.)Google Scholar
Chadburn, R.G., Cooper, D.R. & Habermas, S. (1995). Actuarial mathematics: core reading for subject D. Institute and Faculty of Actuaries, Oxford.Google Scholar
Currie, I.D. & Waters, H.R. (1991). On modelling select mortality. J.I.A. 118, 453–81.Google Scholar
Forfar, D.O., Mccutcheon, J.J. & Wilkie, A.D. (1988). On graduation by mathematical formula. J.I.A. 115, 1–149 and 693–698 and T.F.A. 41, 97–269.Google Scholar
Hill, C., Laplanche, A. & Rezvani, A. (1985). Comparison of a cohort with the mortality of a reference population in a prognostic study. Statistics in Medicine, 4, 295.CrossRefGoogle Scholar
Renshaw, A.E. (1988). Modelling excess mortality using GLIM. J.I.A. 115, 299–315.Google Scholar
Roberts, L.A. (1986). Bias in décrémentai rate estimates. O.A.R.D. No. 38, Institute of Actuaries.Google Scholar
Scott, W.F. (1982). Some applications of the Poisson distribution in mortality studies. T.F.A. 38, 255–63.Google Scholar
Scott, W.F. (1991). Some statistical aspects of the Continuous Mortality Investigation Bureau's mortality investigations. J.I.A. 118, 483–8.Google Scholar