Support Vector Machines as tools for mortality graduation

Anastasia Kostaki, Javier M. Moguerza, Alberto Olivares, Stelios Psarakis

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.

Keywords


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

Full Text:

PDF

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.

Canadian Studies in Population | E-ISSN 1927-629X

Copyright © Canadian Population Society