IDEAS home Printed from https://ideas.repec.org/a/spr/stmapp/v32y2023i5d10.1007_s10260-023-00714-0.html
   My bibliography  Save this article

A multi-way analysis of similarity patterns in longevity improvements

Author

Listed:
  • Giovanni Cardillo

    (Sapienza University of Rome)

  • Paolo Giordani

    (Sapienza University of Rome)

  • Susanna Levantesi

    (Sapienza University of Rome)

  • Andrea Nigri

    (University of Foggia)

  • Alessandro Spelta

    (Pavia University)

Abstract

With the increasing availability of temporal data, a researcher often analyzes information saved into matrices, in which entries are replicated in different occasions. Such multidimensional data can be stored in arrays or tensors in a way that relevant patterns among variables can be teased apart by retaining the time-varying nature of the data. In this work, we show how the nonnegative three-way DEcomposition into DIrectional COMponents (DEDICOM) model is able to extract meaningful relational patterns from multi-population mortality data. The dataset considered is provided by the human mortality database (HMD) and refers to three dimensions: countries, age groups and years. The three-dimensional decomposition technique identifies persistent groups of countries with homogeneous mortality behaviours related to the evolutionary process of longevity improvements. Moreover, we exploit both country group information and recurrent neural networks to forecast future trajectories of similarities among countries’ mortality. Our work, by specifically describing the mesoscale interactions between countries and their evolution in time, could help to design appropriate actions against longevity risk that may impact the stability conditions of life assurance and pensions.

Suggested Citation

  • Giovanni Cardillo & Paolo Giordani & Susanna Levantesi & Andrea Nigri & Alessandro Spelta, 2023. "A multi-way analysis of similarity patterns in longevity improvements," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(5), pages 1805-1828, December.
    • Handle: RePEc:spr:stmapp:v:32:y:2023:i:5:d:10.1007_s10260-023-00714-0
    DOI: 10.1007/s10260-023-00714-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10260-023-00714-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10260-023-00714-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. France Meslé & Jacques Vallin, 2017. "The End of East–West Divergence in European Life Expectancies? An Introduction to the Special Issue," European Journal of Population, Springer;European Association for Population Studies, vol. 33(5), pages 615-627, December.
    2. Daniel D. Lee & H. Sebastian Seung, 1999. "Learning the parts of objects by non-negative matrix factorization," Nature, Nature, vol. 401(6755), pages 788-791, October.
    3. Spelta, Alessandro & Araújo, Tanya, 2012. "The topology of cross-border exposures: Beyond the minimal spanning tree approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(22), pages 5572-5583.
    4. J. Carroll & Jih-Jie Chang, 1970. "Analysis of individual differences in multidimensional scaling via an n-way generalization of “Eckart-Young” decomposition," Psychometrika, Springer;The Psychometric Society, vol. 35(3), pages 283-319, September.
    5. Csaba G. Tóth, 2021. "Multi-population models to handle mortality crises in forecasting mortality: A case study from Hungary," Society and Economy, Akadémiai Kiadó, Hungary, vol. 43(2), pages 128-146, June.
    6. Carter, Lawrence R. & Lee, Ronald D., 1992. "Modeling and forecasting US sex differentials in mortality," International Journal of Forecasting, Elsevier, vol. 8(3), pages 393-411, November.
    7. Ledyard Tucker, 1966. "Some mathematical notes on three-mode factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 31(3), pages 279-311, September.
    8. Maria Russolillo & Giuseppe Giordano & Steven Haberman, 2011. "Extending the Lee–Carter model: a three-way decomposition," Scandinavian Actuarial Journal, Taylor & Francis Journals, vol. 2011(2), pages 96-117.
    9. Richard A. Harshman & Paul E. Green & Yoram Wind & Margaret E. Lundy, 1982. "A Model for the Analysis of Asymmetric Data in Marketing Research," Marketing Science, INFORMS, vol. 1(2), pages 205-242.
    10. Nan Li & Ronald Lee, 2005. "Coherent mortality forecasts for a group of populations: An extension of the lee-carter method," Demography, Springer;Population Association of America (PAA), vol. 42(3), pages 575-594, August.
    11. Yumo Dong & Fei Huang & Honglin Yu & Steven Haberman, 2020. "Multi-population mortality forecasting using tensor decomposition," Scandinavian Actuarial Journal, Taylor & Francis Journals, vol. 2020(8), pages 754-775, September.
    12. Andrea Nigri & Elisabetta Barbi & Susanna Levantesi, 2022. "The relay for human longevity: country-specific contributions to the increase of the best-practice life expectancy," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(6), pages 4061-4073, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Giovanni Cardillo & Paolo Giordani & Susanna Levantesi & Andrea Nigri, 2024. "A tensor-based approach to cause-of-death mortality modeling," Annals of Operations Research, Springer, vol. 342(3), pages 2075-2094, November.
    2. repec:osf:socarx:8u34d_v1 is not listed on IDEAS
    3. Richard Harshman & Margaret Lundy, 1996. "Uniqueness proof for a family of models sharing features of Tucker's three-mode factor analysis and PARAFAC/candecomp," Psychometrika, Springer;The Psychometric Society, vol. 61(1), pages 133-154, March.
    4. Giudici, Paolo & Huang, Bihong & Spelta, Alessandro, 2019. "Trade networks and economic fluctuations in Asian countries," Economic Systems, Elsevier, vol. 43(2), pages 1-1.
    5. Basellini, Ugofilippo & Camarda, Carlo Giovanni & Booth, Heather, 2023. "Thirty years on: A review of the Lee–Carter method for forecasting mortality," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1033-1049.
    6. Søren Kjærgaard & Yunus Emre Ergemen & Marie-Pier Bergeron Boucher & Jim Oeppen & Malene Kallestrup-Lamb, 2019. "Longevity forecasting by socio-economic groups using compositional data analysis," CREATES Research Papers 2019-08, Department of Economics and Business Economics, Aarhus University.
    7. Zhang, Xuanming & Huang, Fei & Hui, Francis K.C. & Haberman, Steven, 2023. "Cause-of-death mortality forecasting using adaptive penalized tensor decompositions," Insurance: Mathematics and Economics, Elsevier, vol. 111(C), pages 193-213.
    8. Francesca Perla & Salvatore Scognamiglio, 2023. "Locally-coherent multi-population mortality modelling via neural networks," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 46(1), pages 157-176, June.
    9. Norkhairunnisa Redzwan & Rozita Ramli, 2022. "A Bibliometric Analysis of Research on Stochastic Mortality Modelling and Forecasting," Risks, MDPI, vol. 10(10), pages 1-17, October.
    10. Basellini, Ugofilippo & Camarda, Carlo Giovanni & Booth, Heather, 2022. "Thirty years on: A review of the Lee-Carter method for forecasting mortality," SocArXiv 8u34d, Center for Open Science.
    11. Mariela González-Narváez & María José Fernández-Gómez & Susana Mendes & José-Luis Molina & Omar Ruiz-Barzola & Purificación Galindo-Villardón, 2021. "Study of Temporal Variations in Species–Environment Association through an Innovative Multivariate Method: MixSTATICO," Sustainability, MDPI, vol. 13(11), pages 1-25, May.
    12. de Jong, Piet & Tickle, Leonie & Xu, Jianhui, 2020. "A more meaningful parameterization of the Lee–Carter model," Insurance: Mathematics and Economics, Elsevier, vol. 94(C), pages 1-8.
    13. Elisa Frutos-Bernal & Ángel Martín del Rey & Irene Mariñas-Collado & María Teresa Santos-Martín, 2022. "An Analysis of Travel Patterns in Barcelona Metro Using Tucker3 Decomposition," Mathematics, MDPI, vol. 10(7), pages 1-17, March.
    14. Yoshio Takane & Forrest Young & Jan Leeuw, 1977. "Nonmetric individual differences multidimensional scaling: An alternating least squares method with optimal scaling features," Psychometrika, Springer;The Psychometric Society, vol. 42(1), pages 7-67, March.
    15. David Blake & Marco Morales & Enrico Biffis & Yijia Lin & Andreas Milidonis, 2017. "Special Edition: Longevity 10 – The Tenth International Longevity Risk and Capital Markets Solutions Conference," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 84(S1), pages 515-532, April.
    16. Giuseppe Brandi & Ruggero Gramatica & Tiziana Di Matteo, 2019. "Unveil stock correlation via a new tensor-based decomposition method," Papers 1911.06126, arXiv.org, revised Apr 2020.
    17. Katrien Antonio & Anastasios Bardoutsos & Wilbert Ouburg, 2015. "Bayesian Poisson log-bilinear models for mortality projections with multiple populations," BAFFI CAREFIN Working Papers 1505, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    18. Koissi, Marie-Claire & Shapiro, Arnold F., 2006. "Fuzzy formulation of the Lee-Carter model for mortality forecasting," Insurance: Mathematics and Economics, Elsevier, vol. 39(3), pages 287-309, December.
    19. repec:osf:osfxxx:z3qmw_v1 is not listed on IDEAS
    20. Blake, David & Cairns, Andrew J.G., 2021. "Longevity risk and capital markets: The 2019-20 update," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 395-439.
    21. French, Declan, 2014. "International mortality modelling—An economic perspective," Economics Letters, Elsevier, vol. 122(2), pages 182-186.
    22. Schinzinger, Edo & Denuit, Michel M. & Christiansen, Marcus C., 2016. "A multivariate evolutionary credibility model for mortality improvement rates," Insurance: Mathematics and Economics, Elsevier, vol. 69(C), pages 70-81.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:stmapp:v:32:y:2023:i:5:d:10.1007_s10260-023-00714-0. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.