Support Vector Machines as tools for mortality graduation
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.
mortality pattern ; graduation techniques ; support vector machines ; kernel regression estimators
- There are currently no refbacks.
is licensed under a Creative Commons Attribution 3.0 License
Canadian Studies in Population | E-ISSN 1927-629X
Copyright © Canadian Population Society