Support Vector Machines as tools for mortality graduation

Authors

  • Anastasia Kostaki Athens University of Economics and Business
  • Javier M. Moguerza Rey Juan Carlos University
  • Alberto Olivares
  • Stelios Psarakis

DOI:

https://doi.org/10.25336/P6VS46

Keywords:

mortality pattern, graduation techniques, support vector machines, kernel regression estimators

Abstract

A topic of interest in demographic and biostatistical analysis as well as in actuarial practice,is the graduation of the age-specific mortality pattern. A classical graduation technique is to fit parametric models. Recently, particular emphasis has been given to graduation using nonparametric techniques. Support Vector Machines (SVM) is an innovative methodology that could be utilized for mortality graduation purposes. This paper evaluates SVM techniques as tools for graduating mortality rates. We apply SVM to empirical death rates from a variety of populations and time periods. For comparison, we also apply standard graduation techniques to the same data.

Author Biography

Anastasia Kostaki, Athens University of Economics and Business



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Published

2012-07-05