10.57647/mathsci.2025.1901.02

Sushila-Poisson Distribution: A Flexible Tool for Survival and Reliability Modeling

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  • Sepideh Daghagh1,
  • Anis Iranmanesh*,
  • Najmeh Nakhaei Rad2,
  • Ehsan Ormoz1,
  1. Department of Mathematics and Computer Sciences, Ma.C., Islamic Azad University, Mashhad, Iran
  2. Department of Statistics, University of Pretoria, Pretoria, South Africa

Received: 2025-01-06

Revised: 2025-02-14

Accepted: 2025-02-28

Published in Issue 2025-03-31

Copyright (c) 2025 Sepideh Daghagh, Anis Iranmanesh, Najmeh Nakhaei Rad, Ehsan Ormoz (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Daghagh, S., Iranmanesh, A., Nakhaei Rad, N., & Ormoz, E. (2025). Sushila-Poisson Distribution: A Flexible Tool for Survival and Reliability Modeling. Mathematical Sciences, 19(1 (March 2025). https://doi.org/10.57647/mathsci.2025.1901.02
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Abstract

This paper presents a new type of Sushila distribution that provides greater flexibility for modelling lifetime data. This model, called the Sushila-Poisson (SP) distribution, is created by combining the Sushila and Poisson distributions. The three-parameter SP distribution represents various shapes of hazard rate functions, including upside-down bathtub, bathtub-shaped, increasing, and decreasing hazard rates, which are commonly encountered in fields such as medicine, engineering, economics, and the natural sciences. Therefore, the proposed model offers great potential for applications in these areas. The new model includes several known distributions, such as the Lindley, Lindley-Poisson, and Sushila distributions, as special cases. Several statistical properties of the SP distribution have been derived in this study. Simulation studies were conducted to examine the performance of the maximum likelihood estimators, which were developed using the Expectation-Maximization (EM) algorithm. The flexibility of the new model was further demonstrated through its application to three real data sets. 

Keywords

  • EM algorithm,
  • Maximum likelihood estimation,
  • Poisson distribution,
  • Sushila distribution
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