Multi-Country Mortality Analysis Using Self Organizing Maps
In this paper we introduce the use of Self Organizing Maps (SOMs) in multidimensional mortality analysis. The rationale behind this contribution is that patterns of mortality in different areas of the world are becoming more and more related; a fast and intuitive method understanding the similarities among mortality experiences could therefore be of aid to improve the knowledge on this complex phenomenon. The results we have obtained highlight common features in the mortality experience of various countries, hence supporting the idea that SOM may be a very effective tool in this field.
KeywordsClustering Mortality Self Organizing Maps
Unable to display preview. Download preview PDF.
- 3.Fiig Jarner, S., Masotty Kryger, E.: Modelling adult mortality in small populations: the Saint model. Pensions Institute Discussion Paper PI-0902 (2009)Google Scholar
- 6.Kaski, S., Kangas, J., Kohonen, T.: Bibliography of Self-Organizing Map (SOM) Papers: 1981-1997. Neur. Comp. Surveys 1, 102–350 (1998)Google Scholar
- 7.Kohonen, T.: Self-Organizing Maps, 3rd extended edn. Springer, Berlin (2001)Google Scholar
- 11.Oja, M., Kaski, S., Kohonen, T.: Bibliography of Self-Organizing Map (SOM) Papers: 1998-2001 Addendum. Neur. Comp. Surveys 3, 1–156 (2003)Google Scholar
- 12.Polla, M., Honkela, T., Kohonen, T.: Bibliography of Self-Organizing Map (SOM) Papers: 2002-2005 Addendum. TKK Reports in Information and Computer Science, Helsinki University of Technology, Report TKK-ICS-R23 (2009)Google Scholar
- 13.Resta, M.: Early Warning Systems: an approach via Self Organizing Maps with applications to emergent markets. In: Proc. of the 2009 Conference on New Directions in Neural Networks: 18th Italian Workshop on Neural Networks, WIRN 2008, pp. 176–184. IOS Press, Amsterdam (2009)Google Scholar
- 18.Wilson, C.: On the Scale of Global Demographic Convergence 19502000. Pop. and Dev. Rev. 24, 593–600 (2001)Google Scholar