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The Lindley Gompertz Model for Estimating the Survival Rates: Properties and Applications in Insurance

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Abstract

This paper introduces a new extension of the Gompertz function for estimating the survival rates. The actual survival rates from USA life tables 2015 is considered for assessment process under the ordinary least squares method. A real data application is presented under the maximum likelihood method. The new Gompertz function is compared with many other competitive ones such as the Gompertz, the exponentiated Gompertz, the Rayleigh Gompertz, Weibull Gompertz, the Burr type X Gompertz and Rayleigh generalized Gompertz models.

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Data Availability

This work is basically a methodological development in the actuarial scinces and has been applied on actuarial secondary data, but if required, data will be provided.

Code availability

The codes in this paper represent a new development on the “R” and “Mathcad” programs, and we will provide them if requested.

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Acknowledgements

The authors would like to thank the reviewer for the thorough comments on the manuscript, improving it in several aspects.

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No funding received.

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Authors and Affiliations

  1. Department of Statistics and Quantitative Methods, Faculty of Business Administration, Horus University, Damietta, Egypt

    Heba Soltan Mohamed

  2. Department of Mathematical Sciences, Ball State University, Muncie, IN, USA

    M. Masoom Ali

  3. Department of Statistics, Mathematics and Insurance, Benha University, Benha, Egypt

    Haitham M. Yousof

Authors
  1. Heba Soltan Mohamed
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  2. M. Masoom Ali
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  3. Haitham M. Yousof
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Contributions

Heba Soltan Mohamed (review and editing, validation, conceptualization, project administration), M. Masoom Ali (review and editing & validation) and Haitham M. Yousof (writing the original draft preparation, validation, conceptualization, project administration).

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Correspondence to Haitham M. Yousof.

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Mohamed, H.S., Ali, M.M. & Yousof, H.M. The Lindley Gompertz Model for Estimating the Survival Rates: Properties and Applications in Insurance. Ann. Data. Sci. 10, 1199–1216 (2023). https://doi.org/10.1007/s40745-022-00450-4

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  • DOI: https://doi.org/10.1007/s40745-022-00450-4

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