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Peer-reviewedCC BY-NCReliability: Theory & Applications

A New Extended Exponential Rayleigh Distribution: Estimation and Application

  • Dinesh Kumar1
  • Omprakash Prajapati1
  • Sultan Parveen2
  • Abhijit Sahoo1
  1. 1Department of Statistics, Banaras Hindu University, Varanasi 221005, India
  2. 2Department of Mathematics and Statistics, Aliah University, Kolkata 700160, India
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Abstract

In the present paper, we have introduced a new extended exponential Rayleigh distribution with the help of the Transformed-Transformer technique using traditional exponential and Rayleigh distributions, and some important statistical properties of the newly developed distribution have been studied. In addition, the parameters of the distribution have been estimated through the maximum likelihood, least squares, and maximum product of spacing methods of estimation on the basis of complete samples of it. Two real data sets have been considered in order to show the applicability of the newly developed distribution to the real life problems.

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Cite

BibTeXRIS
BibTeX
@article{kumar2026extended,
  title = {{A New Extended Exponential Rayleigh Distribution: Estimation and Application}},
  author = {Dinesh Kumar and Omprakash Prajapati and Sultan Parveen and Abhijit Sahoo},
  journal = {Reliability: Theory \& Applications},
  year = {2026},
  month = sep,
  doi = {10.66721/r68zxp66},
  url = {https://doi.org/10.66721/r68zxp66},
  note = {CC BY-NC},
}
RIS
TY  - JOUR
TI  - A New Extended Exponential Rayleigh Distribution: Estimation and Application
AU  - Dinesh Kumar
AU  - Omprakash Prajapati
AU  - Sultan Parveen
AU  - Abhijit Sahoo
PY  - 2026
DA  - 2026/09/01
JO  - Reliability: Theory & Applications
DO  - 10.66721/r68zxp66
UR  - https://doi.org/10.66721/r68zxp66
AB  - In the present paper, we have introduced a new extended exponential Rayleigh distribution with the help of the Transformed-Transformer technique using traditional exponential and Rayleigh distributions, and some important statistical properties of the newly developed distribution have been studied. In addition, the parameters of the distribution have been estimated through the maximum likelihood, least squares, and maximum product of spacing methods of estimation on the basis of complete samples of it. Two real data sets have been considered in order to show the applicability of the newly developed distribution to the real life problems.
SN  - 1932-2321
ER  -