A new heavy-tailed generalised exponentiated half logistic-G distribution with actuarial measures and applications

This work introduces and investigates the new heavy-tailed generalised exponentiated half logistic-G (HT-GEN-EHL-G) family of distributions. The study involves the derivation and analysis of statistical properties associated with HT-GEN-EHL-G distribution. Employing the maximum likelihood estimation technique, we estimate model parameters and evaluate the consistency of these estimators through simulation studies. Additionally, we develop actuarial metrics (risk measures) tailored to this distribution. The practical utility of the HT-GEN-EHL-G family of distributions is demonstrated through the analysis of four real-life datasets from diverse fields. These applications emphasise the significance and versatility of the newly introduced HT-GEN-EHL-G family of distributions.

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Publication Details

Journal
International Journal of Mathematics in Operational Research
Published
2025-01-01
DOI
https://doi.org/10.1504/ijmor.2025.146356
Primary Topic
Statistical Distribution Estimation and Applications
Type
article
Field-Weighted Citation Impact
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article

A new heavy-tailed generalised exponentiated half logistic-G distribution with actuarial measures and applications

Broderick Olusegun Oluyede, Thatayaone Moakofi, Agolame Puoetsile
International Journal of Mathematics in Operational Research
Statistical Distribution Estimation and Applications
article

A new heavy-tailed generalised exponentiated half logistic-G distribution with actuarial measures and applications

Broderick Olusegun Oluyede, Thatayaone Moakofi, Agolame Puoetsile
article en

Abstract

This work introduces and investigates the new heavy-tailed generalised exponentiated half logistic-G (HT-GEN-EHL-G) family of distributions. The study involves the derivation and analysis of statistical properties associated with HT-GEN-EHL-G distribution. Employing the maximum likelihood estimation technique, we estimate model parameters and evaluate the consistency of these estimators through simulation studies. Additionally, we develop actuarial metrics (risk measures) tailored to this distribution. The practical utility of the HT-GEN-EHL-G family of distributions is demonstrated through the analysis of four real-life datasets from diverse fields. These applications emphasise the significance and versatility of the newly introduced HT-GEN-EHL-G family of distributions.

International Journal of Mathematics in Operational ResearchVol. 30(4)
Botswana International University of Science and Technology (BW)
Openalex Percentile: Top 88%
Statistical Distribution Estimation and Applications
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