@article{Kostaki_20_25,
author = {Kostaki, Anastasia and Moguerza, Javier and Olivares, Alberto and Psarakis, Stelios},
title={{Graduating the age-specific fertility pattern using Support Vector Machines}},
journal = {Demographic Research},
volume = {20},
number = {25},
pages = {599--622},
doi = {10.4054/DemRes.2009.20.25},
year = {2009},
abstract = {A topic of interest in demographic literature is the graduation of the age-specific fertility pattern. A standard graduation technique extensively used by demographers is to fit parametric models that accurately reproduce it. Non-parametric statistical methodology might be alternatively used for this graduation purpose.   Support Vector Machines (SVM) is a non-parametric methodology that could be utilized for fertility graduation purposes. This paper evaluates the SVM techniques as tools for graduating fertility rates In that we apply these techniques to empirical age specific fertility rates from a variety of populations, time period, and cohorts. Additionally, for comparison reasons we also fit known parametric models to the same empirical data sets.},
URL = {https://www.demographic-research.org/volumes/vol20/25/},
eprint = {https://www.demographic-research.org/volumes/vol20/25/20-25.pdf}
}