JOURNAL ARTICLE

A New Inverse Extended Weibull Distribution for Modelling Insurance Loss Data.

  • Published In: International Journal of Uncertainty, Fuzziness & Knowledge-Based Systems, 2023, v. 31. P. 307 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Yu, Shilin; Li, Xuan; Choy, S. T. Boris 3 of 3

Abstract

This paper proposes a new three-parameter inverse Weibull-type distribution via a similar way of constructing a skew normal distribution. The additional shape parameter increases the distribution's modelling capability as other three-parameter inverse Weibull-type distributions. The mathematical properties of this inverse extended Weibull distribution are studied and proven. In the empirical study of four insurance datasets from Australia, Denmark and the USA, we show that the proposed distribution performs very well compared with other three-parameter competitors. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Uncertainty, Fuzziness & Knowledge-Based Systems. 2023/12, Vol. 31, p307
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2023
  • ISSN:0218-4885
  • DOI:10.1142/S0218488523400196
  • Accession Number:175256844
  • Copyright Statement:Copyright of International Journal of Uncertainty, Fuzziness & Knowledge-Based Systems is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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