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New extensions of Rayleigh distribution based on inverted-Weibull and Weibull distributions

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 11 No. 6 (2021)

DOI
10.11591/ijece.v11i6.pp5107-5118
ISSN
2722-2578, 2088-8708
Published
2021-12-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-sa/4.0

Abstract

The Rayleigh distribution was proposed in the fields of acoustics and optics by lord Rayleigh. It has wide applications in communication theory, such as description of instantaneous peak power of received radio signals, i.e. study of vibrations and waves. It has also been used for modeling of wave propagation, radiation, synthetic aperture radar images, and lifetime data in engineering and clinical studies. This work proposes two new extensions of the Rayleigh distribution, namely the Rayleigh inverted-Weibull (RIW) and the Rayleigh Weibull (RW) distributions. Several fundamental properties are derived in this study, these include reliability and hazard functions, moments, quantile function, random number generation, skewness, and kurtosis. The maximum likelihood estimators for the model parameters of the two proposed models are also derived along with the asymptotic confidence intervals. Two real data sets in communication systems and clinical trials are analyzed to illustrate the concept of the proposed extensions. The results demonstrated that the proposed extensions showed better fitting than other extensions and competing models.

Collaboration network of the authors

8 authors · 9 co-authorship links · 5 across different organisations

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  • Jordan University of Science and Technology
  • University of Tabuk
  • Arabian Gulf University
  • Hashemite University
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Saiful Islam AnsariUniversity of Tabuk1
Ahmed JaradatArabian Gulf University1
Mohammad H. AlmomaniHashemite University1
Sarah N. Alabed AlhadiJordan University of Science and Technology1
Nour AlsumairatJordan University of Science and Technology1
Marwa Z. TuffahaJordan University of Science and Technology1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
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