On fitting generalized linear and non-linear models of mortality

Iain D. Currie

Research output: Contribution to journalArticle

28 Citations (Scopus)

Abstract

Many common models of mortality can be expressed compactly in the language of either generalized linear models or generalized non-linear models. The R language provides a description of these models which parallels the usual algebraic definitions but has the advantage of a transparent and flexible model specification. We compare eight model structures for mortality. For each structure, we consider (a) the Poisson models for the force of mortality with both log and logit link functions and (b) the binomial models for the rate of mortality with logit and complementary log-log link functions. Part of this work shows how to extend the usual smooth two-dimensional P-spline model for the force of mortality with Poisson error and log link to the other smooth two-dimensional P-spline models with Poisson and binomial errors defined in (a) and (b). Our comments are based on the results of fitting these models to data from six countries: Australia, France, Japan, Sweden, UK and USA. We also discuss the possibility of forecasting with these models; in particular, the introduction of cohort terms generally leads to an improvement in overall fit, but can also make forecasting with these models problematic.

Original languageEnglish
Pages (from-to)356-383
Number of pages28
JournalScandinavian Actuarial Journal
Volume2016
Issue number4
Early online date4 Jul 2014
DOIs
Publication statusPublished - 2016

Keywords

  • constraints
  • forecasting
  • generalized linear models
  • identifiability
  • mortality
  • R language

ASJC Scopus subject areas

  • Economics and Econometrics
  • Statistics, Probability and Uncertainty
  • Statistics and Probability

Fingerprint Dive into the research topics of 'On fitting generalized linear and non-linear models of mortality'. Together they form a unique fingerprint.

  • Cite this