Exploring the Fréchet–Power Rayleigh Distribution with Statistical Properties and Medical Data Insights

In this article, we introduce a new form of Power Rayleigh distribution using the Topp–Leone generated family of distributions. The new distribution is called the Fréchet–Power Rayleigh distribution. The different structural properties of the distribution are discussed, including moments, moment-generating function, incomplete moments, order statistics, Rényi entropy, and mean deviations. The estimation of the parameters is performed by using the classical maximum likelihood method. A simulation analysis is carried out to evaluate and compare the effectiveness of estimators in terms of their bias, variance and mean square error. Finally, the distribution performance and application are investigated using real-life data from medical science.

Authors

Institutions

Publication Details

Journal
Lobachevskii Journal of Mathematics
Published
2024-12-01
DOI
https://doi.org/10.1134/s1995080224607550
Citations
4
Primary Topic
Statistical Distribution Estimation and Applications
Type
article
Field-Weighted Citation Impact
2.84
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Exploring the Fréchet–Power Rayleigh Distribution with Statistical Properties and Medical Data Insights

Raymond Benjamin Afful, Aafaq A. Rather, Andrei Volodin, Imran Ali et al.
4 citations
Lobachevskii Journal of Mathematics
Statistical Distribution Estimation and Applications
2.84
article

Exploring the Fréchet–Power Rayleigh Distribution with Statistical Properties and Medical Data Insights

Raymond Benjamin Afful, Aafaq A. Rather, Andrei Volodin, Imran Ali, Aijaz Ahmad
article en
4 citations

Abstract

In this article, we introduce a new form of Power Rayleigh distribution using the Topp–Leone generated family of distributions. The new distribution is called the Fréchet–Power Rayleigh distribution. The different structural properties of the distribution are discussed, including moments, moment-generating function, incomplete moments, order statistics, Rényi entropy, and mean deviations. The estimation of the parameters is performed by using the classical maximum likelihood method. A simulation analysis is carried out to evaluate and compare the effectiveness of estimators in terms of their bias, variance and mean square error. Finally, the distribution performance and application are investigated using real-life data from medical science.

Lobachevskii Journal of MathematicsVol. 45(12)
University of Kashmir (IN), Aligarh Muslim University (IN), University of Regina (CA), Symbiosis International University (IN)
Openalex Percentile: Top 8%
Statistical Distribution Estimation and Applications
2.84
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.