Advertisement

Adaptive Neuro-Fuzzy Inference Systems vs. Stochastic Models for Mortality Data

  • Valeria D’Amato
  • Gabriella Piscopo
  • Maria Russolillo
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 26)

Abstract

A comparative analysis is done between stochastic models and Adaptive Neuro–Fuzzy Inference System applied to the projection of the longevity trend. The stochastic models provides the heuristic rule for obtaining projections. In the context of ANFIS models, the fuzzy logic allows for determining the learning algorithm on the basis of the relationship between inputs and outputs. In other words the rule is here deducted by the actual mortality data, because this allows for fuzzy systems to learn from the data they are modelling. This is possible by computing the membership function parameters that best allow the associated fuzzy inference system to track the input/output data. The literature indicates that the self-predicting model of ANFIS is better than other models in a lot of fields. Shortcomings and advantages of both approaches are here highlighted.

Keywords

Adaptive Neuro-Fuzzy Inference System Stochastic Models Longevity Projections 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Atsalakis, G., Nezis, D., Matalliotakis, G., Ucenic, C.I., Skiadas, C.: Forecasting Mortality Rate Using a Neural Network with Fuzzy Inference System. Working Papers 806 from University of Crete, Department of Economics (2007)Google Scholar
  2. 2.
    Booth, H.: Demographic forecasting: 1908 to 2005 in review. International Journal of Forecasting 22, 547–581 (2006)CrossRefGoogle Scholar
  3. 3.
    Booth, H., Maindonald, J., Smith, L.: Applying Lee-Carter under conditions of variable mortality decline. Population Studies 56(3), 325–336 (2002), doi:10.1080/00324720215935CrossRefGoogle Scholar
  4. 4.
    Butt, Z., Haberman, S.: A comparative study of parametric mortality projection models (Report No. Actuarial Research Paper No. 196). London, UK: Faculty of Actuarial Science & Insurance, City University London (2010)Google Scholar
  5. 5.
    Coletti, G., Scozzafava, R.: Characterization of Coherent Conditional Probabilities as a Tool for their Assessment and Extension. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 4, 103–127 (1996)CrossRefMATHMathSciNetGoogle Scholar
  6. 6.
    Coletti, G., Scozzafava, R., Vantaggi, B.: Soft Computing: State of the Art Theory and Novel Applications. STUDFUZZ, vol. 291, pp. 193–208. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  7. 7.
    Currie, I.D., Durban, M., Eilers, P.H.C.: Smoothing and forecasting mortality rates. Statistical Modelling 4(4), 279–298 (2004), doi:10.1191/1471082X04st080oaCrossRefMATHMathSciNetGoogle Scholar
  8. 8.
    Delwarde, A., Denuit, M., Eilers, P.: Smoothing the Lee-Carter and Poisson log-bilinear models for mortality forecasting: a penalised log-likelihood approach. Statistical Modelling 7, 29–48 (2007)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Diamond, P.: Fuzzy least-squares. Information Sciences 3, 141–157 (1988)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Hatzopoulos, P., Haberman, S.: A dynamic parameterization modelling for the age-period-cohort mortality. Insurance: Mathematics and Economics 49(2), 155–174 (2011)MATHMathSciNetGoogle Scholar
  11. 11.
    Human Mortality Database. University of California, Berkeley (USA), and Max Planck Institute for Demographic Research (Germany), http://www.mortality.org, http://www.mortality.org (referred to the period 2001 to 2006)
  12. 12.
    Hyndman, R.J., Ullah, S.: Robust forecasting of mortality and fertility rates: a functional data approach. Computational Statistics and Data Analysis 51, 4942–4956 (2007)CrossRefMATHMathSciNetGoogle Scholar
  13. 13.
    Jang, J.S.R.: ANFIS: Adaptive-Network-based Fuzzy Inference Systems. IEEE Trans. on Systems, Man, and Cybernetics 23, 665–685 (1993)CrossRefGoogle Scholar
  14. 14.
    Lee, R.D., Carter, L.R.: Modelling and Forecasting U.S. Mortality. Journal of the American Statistical Association 87, 659–671 (1992)Google Scholar
  15. 15.
    Scozzafava, R., Vantaggi, B.: Fuzzy Inclusion and Similarity through Coherent Conditional Probability. Fuzzy Sets and Systems 160, 292–305 (2009)CrossRefMATHMathSciNetGoogle Scholar
  16. 16.
    Skiadas, C., Matalliotakis, G., Skiadas, C.: An extended quadratic health state function and the related density function for life table data. In: Skiadas, C. (ed.) Recent Advances in Stochastic Modeling and Data Analysis, pp. 360–369. World Scientific, Singapore (2007)CrossRefGoogle Scholar
  17. 17.
    Tanaka, H., Uejima, S., Asai, K.: Linear Regression Analysis with Fuzzy Model. IEEE Transactions on Systems, Man and Cybernetics 12(6), 903–907 (1982)CrossRefMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Valeria D’Amato
    • 1
  • Gabriella Piscopo
    • 2
  • Maria Russolillo
    • 1
  1. 1.Department of Economics and StatisticsUniversity of SalernoSalernoItaly
  2. 2.Department of EconomicsUniversity of GenoaGenoaItaly

Personalised recommendations