Issue |
BIO Web Conf.
Volume 97, 2024
Fifth International Scientific Conference of Alkafeel University (ISCKU 2024)
|
|
---|---|---|
Article Number | 00116 | |
Number of page(s) | 7 | |
DOI | https://doi.org/10.1051/bioconf/20249700116 | |
Published online | 05 April 2024 |
Transformation of Rayleigh Distribution, Properties, and Application
1 Department of Mathematics, Faculty of Education, University of Kufa, Iraq
2 Department of Postgraduate studies, University of Kufa, Iraq
3 Department of Economy, Faculty of Management and Economy, University of Kufa, Iraq
4 Department of Computer Techniques Engineering, University of Alkafeel, Iraq
* Corresponding Author: ahmeda.aladilee@uokufa.edu.iq
The need to develop the theory of statistics and its properties follows from the fact that many types of data cannot be fitted by classical distributions. This fact invites many researchers to generate new distributions, find their properties, and implement a data set to find the best distribution that can fit the data better.
In this paper, we propose special cases of Rayleigh distribution and their relationship to wellknown distributions like half-logistic distribution (HLD), generalized half-logistic distribution (GHLD), and exponentiated half-logistic distribution (EHLD). We have mainly discussed the relationship of a transformation technique of those special cases of Rayleigh distribution with different parameter values to the assigned distributions (HLD, GHLD, EHLD). We also show the mathematical statistical properties of such special cases like the rth moment, central moment, incomplete moments, the probability weighted moments, the stochastic ordering, and interval estimation within the proposed parameters. Consequently, such properties are derived to generate modern statistical characteristics related to the special cases of Rayleigh distribution. Moreover, we have set table for the calculations of particular cases with their derived moments that have previously found their theoretical representations. Finally, we set off some conclusions related to the results of this humble work.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.