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A Net Premium Model for Life Insurance Under a Sort of Generalized Uncertain Interest Rates

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Uncertainty Modelling in Data Science (SMPS 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 832))

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Abstract

In this paper, we apply the LR-fuzzy random variables to estimate a discount function that associated with a generalized interest rate as well as fuzzy random variable future lifetimes, and establish some life annuity and endowments models. A novel fuzzy net premium model is obtained. Finally, a statistical simulation is given for illustrating the proposed models.

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Acknowledgment

This research is supported by the NNSF of China under grant number No.11271096. Their financial aid is greatly appreciated.

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Authors and Affiliations

  1. Department of Statistics, School of Economics and Statistics, Guangzhou University, No. 230 Waihuanxi Road, Higher Education Mega Center, Guangzhou, 510006, People’s Republic of China

    Dabuxilatu Wang

Authors
  1. Dabuxilatu Wang
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Correspondence to Dabuxilatu Wang .

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Editors and Affiliations

  1. CNRS, Heudiasyc, Sorbonne universités, Université de technologie de Compiègne, Compiegne, France

    Sébastien Destercke

  2. CNRS, Heudiasyc, Sorbonne universités, Université de technologie de Compiègne, Compiegne, France

    Thierry Denoeux

  3. Department of Statistics and Operational Research and Mathematics Didactics, University of Oviedo, Oviedo, Asturias, Spain

    María Ángeles Gil

  4. Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland

    Przemyslaw Grzegorzewski

  5. Department of Stochastic Methods, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Olgierd Hryniewicz

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Wang, D. (2019). A Net Premium Model for Life Insurance Under a Sort of Generalized Uncertain Interest Rates. In: Destercke, S., Denoeux, T., Gil, M., Grzegorzewski, P., Hryniewicz, O. (eds) Uncertainty Modelling in Data Science. SMPS 2018. Advances in Intelligent Systems and Computing, vol 832. Springer, Cham. https://doi.org/10.1007/978-3-319-97547-4_29

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