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Applying spectral biclustering to mortality data

DIEC, University of Genova, 16126 Genova, Italy
Author to whom correspondence should be addressed.
Academic Editor: Luca Regis
Risks 2017, 5(2), 24;
Received: 6 October 2016 / Revised: 22 March 2017 / Accepted: 29 March 2017 / Published: 4 April 2017
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-Text
Keywords: mortality data; biclustering; cohort effect mortality data; biclustering; cohort effect
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MDPI and ACS Style

Piscopo, G.; Resta, M. Applying spectral biclustering to mortality data. Risks 2017, 5, 24.

AMA Style

Piscopo G, Resta M. Applying spectral biclustering to mortality data. Risks. 2017; 5(2):24.

Chicago/Turabian Style

Piscopo, Gabriella, and Marina Resta. 2017. "Applying spectral biclustering to mortality data" Risks 5, no. 2: 24.

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