Authors:
Duygun Fatih Demirel
and
Melek Basak
Affiliation:
Yeditepe University, Turkey
Keyword(s):
Fuzzy Modeling, Lee-Carter Method, Human Mortality, Singular Value Decomposition, Fuzzy Regression, Unconstrained Nonlinear Optimization.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Computer-Supported Education
;
Domain Applications and Case Studies
;
Fuzzy Systems
;
Fuzzy Systems Design, Modeling and Control
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Industrial, Financial and Medical Applications
;
Mathematical Foundations: Fuzzy Set Theory and Fuzzy Logic
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
Human mortality modeling and forecasting are important study fields since mortality rates are essential in
financial and social policy making. Among many others, Lee Carter (LC) model is one of the most popular
stochastic method in mortality forecasting. Koissi and Shapiro fuzzified the standard LC model and
eliminated the assumptions of homoscedasticity and the ambiguity on the size of the error term variances. In
this study, a modified version of fuzzy LC model incorporating singular value decomposition (SVD)
technique is proposed. Utilizing SVD instead of ordinary least squares in the fuzzy LC model allows the
model to capture existing fluctuations in mortality rates and yields a better fit. The proposed method is
applied to Finland mortality data for years 1925 to 2009. The results are compared with Koissi and
Shapiro’s fuzzy LC method and the standard LC method. Numerical findings show that proposed method
gives statistically better results in generating small spreads and in est
imating mortality rates when compared
with Koissi and Shapiro’s method.
(More)