Description: Using population data, mortality rates are projected for people who will attain age 80 in the future. Finally, the distribution of the maximum age at death that will be attained by a Canadian is investigated.

Description: Logistic-type models for the force of mortality like those introduced by Perks or Kannisto provide better fit to mortality data of people aged over 85 than Makeham’s model where the force of mortality increases exponentially with age. However, the difficulty in estimating their parameters by the maximum likelihood method makes their use less popular among actuaries. For Kannisto’s model, this paper proposes a weighted least-squares estimator which can easily be calculated with any regression software. The estimator is shown to be consistent, asymptotically unbiased and normally distributed. For Perks’ model, using a Taylor’s series expansion, the estimation problem is again reduced to a least-squares problem. The various estimators proposed in the paper are compared numerically using Canadian mortality data.

Description: Logistic-type models for the force of mortality like those introduced by Perks or Kannisto provide better fit to mortality data of people aged over 85 than Makeham’s model where the force of mortality increases exponentially with age. However, the difficulty in estimating their parameters by the maximum likelihood method makes their use less popular among actuaries.

Description: This paper looks at the actuarial implications of a gradual retirement scheme from the employer's point of view in terms of the normal cost and also from the employee's side, the total income he would receive under various gradual retirement scenarios.

Description: In this paper, we introduce a family of leptokurtic symmetric distributions represented by the difference of two gamma variates. Properties of this family are discussed. The Laplace, sums of Laplace and normal distributions all arise as special cases of this family. We propose a two-step method for fitting data to this family. We develop a statistical test for goodness-of-fit and introduce a test of normality of the data. A simulation study is provided to illustrate the theory.