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An introduction to gevistic regression mortality models

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  • Anthony Medford
  • James W. Vaupel

Abstract

Many common stochastic mortality models can be formulated as a generalized linear model (GLM). When these GLMs are used to model one year-death probabilities, $ q_x $ qx, deaths are assumed to be binomially distributed, and the canonical logit link function has been used by default. In this work we present the quantile function of the Generalized Extreme Value distribution as an alternative link function to the standard canonical logit link and show that its theoretical advantages enable a better fit for mortality models in cases when data are highly imbalanced or sparse. We provide an example that shows that this link function also enables superior fits to mortality data at the very highest ages in the case of the Cairns Blake Dowd family of mortality models.

Suggested Citation

  • Anthony Medford & James W. Vaupel, 2019. "An introduction to gevistic regression mortality models," Scandinavian Actuarial Journal, Taylor & Francis Journals, vol. 2019(7), pages 604-620, August.
    • Handle: RePEc:taf:sactxx:v:2019:y:2019:i:7:p:604-620
    DOI: 10.1080/03461238.2019.1586756
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