Risks 2017, 5(2), 24; https://doi.org/10.3390/risks5020024
Applying spectral biclustering to mortality data
DIEC, University of Genova, 16126 Genova, Italy
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Academic Editor: Luca Regis
Received: 6 October 2016 / Revised: 22 March 2017 / Accepted: 29 March 2017 / Published: 4 April 2017
(This article belongs to the Special Issue Actuarial and Financial Risks in Life Insurance, Pensions and Household Finance)
Abstract
We apply spectral biclustering to mortality datasets in order to capture three relevant aspects: the period, the age and the cohort effects, as their knowledge is a key factor in understanding actuarial liabilities of private life insurance companies, pension funds as well as national pension systems. While standard techniques generally fail to capture the cohort effect, on the contrary, biclustering methods seem particularly suitable for this aim. We run an exploratory analysis on the mortality data of Italy, with ages representing genes, and years as conditions: by comparison between conventional hierarchical clustering and spectral biclustering, we observe that the latter offers more meaningful results. View Full-TextKeywords:
mortality data; biclustering; cohort effect
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Piscopo, G.; Resta, M. Applying spectral biclustering to mortality data. Risks 2017, 5, 24.
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