A Novel Extension of Burhan Distribution: Theoretical Properties, Simulation Study and Practical Application

In this paper, we present an innovative extension of Burhan distribution termed as Weighted Burhan distribution. A comprehensive exploration of the distinct structural properties of this newly proposed model is conducted including moments, survival function, hazard function, moment generating function, characteristics function, and cumulant generating function, likelihood ratio test, ordered statistics, and entropy measures. The parameters of the distribution are estimated by using maximum likelihood estimation method. Moreover, simulation methodologies are employed to assess the efficacy and robustness of maximum likelihood estimators. Finally, the performance of the proposed model is evaluated using a real life data set.

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Publication Details

Journal
Lobachevskii Journal of Mathematics
Published
2024-12-01
DOI
https://doi.org/10.1134/s1995080224607574
Citations
1
Primary Topic
Statistical Distribution Estimation and Applications
Type
article
Field-Weighted Citation Impact
0.71
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article

A Novel Extension of Burhan Distribution: Theoretical Properties, Simulation Study and Practical Application

Aafaq A. Rather, Nahaa E. Alsubaie, Eid Sadun Alotaibi, Danish Qayoom
1 citations
Lobachevskii Journal of Mathematics
Statistical Distribution Estimation and Applications
0.71
article

A Novel Extension of Burhan Distribution: Theoretical Properties, Simulation Study and Practical Application

Aafaq A. Rather, Nahaa E. Alsubaie, Eid Sadun Alotaibi, Danish Qayoom
article en
1 citations

Abstract

In this paper, we present an innovative extension of Burhan distribution termed as Weighted Burhan distribution. A comprehensive exploration of the distinct structural properties of this newly proposed model is conducted including moments, survival function, hazard function, moment generating function, characteristics function, and cumulant generating function, likelihood ratio test, ordered statistics, and entropy measures. The parameters of the distribution are estimated by using maximum likelihood estimation method. Moreover, simulation methodologies are employed to assess the efficacy and robustness of maximum likelihood estimators. Finally, the performance of the proposed model is evaluated using a real life data set.

Lobachevskii Journal of MathematicsVol. 45(12)
Taif University (SA), Symbiosis International University (IN)
Openalex Percentile: Top 23%
Statistical Distribution Estimation and Applications
0.71
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