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Applying spectral biclustering to mortality data

Author

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  • Gabriella Piscopo

    (DIEC, University of Genova, 16126 Genova, Italy)

  • Marina Resta

    (DIEC, University of Genova, 16126 Genova, Italy)

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.

Suggested Citation

  • Gabriella Piscopo & Marina Resta, 2017. "Applying spectral biclustering to mortality data," Risks, MDPI, vol. 5(2), pages 1-13, April.
    • Handle: RePEc:gam:jrisks:v:5:y:2017:i:2:p:24-:d:94898
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    References listed on IDEAS

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    5. Renshaw, A.E. & Haberman, S., 2006. "A cohort-based extension to the Lee-Carter model for mortality reduction factors," Insurance: Mathematics and Economics, Elsevier, vol. 38(3), pages 556-570, June.
    6. Hyndman, Rob J. & Shahid Ullah, Md., 2007. "Robust forecasting of mortality and fertility rates: A functional data approach," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 4942-4956, June.
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    Cited by:

    1. Luca Regis, 2017. "Special Issue “Actuarial and Financial Risks in Life Insurance, Pensions and Household Finance”," Risks, MDPI, vol. 5(4), pages 1-2, December.

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