A novel two–parameter unit probability model with properties and applications

This paper develops a novel two-parameter unit probability model which is the generalized form Kumaraswami distribution that exhibits greater flexibility compared to well-known existing distributions, attributed to its distinct hazard and density function shapes. Extensive analysis has been conducted to explore numerous statistical features of the specified distribution, specifically moments, and order statistics providing explicit expressions for these measures. The maximum likelihood estimation is employed to estimate the model parameters and a numerical simulation analysis confirms the consistency of this estimation approach. Furthermore, the applicability of the specified model is demonstrated by considering four real data sets, showcasing its effectiveness in capturing the characteristics of real life data. The proposed model shows promise as a versatile tool for analyzing diverse data sets in a wide range of fields.

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Publication Details

Journal
Heliyon
Published
2024-09-01
DOI
https://doi.org/10.1016/j.heliyon.2024.e37242
Citations
4
Primary Topic
Statistical Distribution Estimation and Applications
Type
article
Field-Weighted Citation Impact
2.84

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article

A novel two–parameter unit probability model with properties and applications

Abdus Saboor, Fuad A. A. Awwad, Farrukh Jamal, Musharraf Ali et al.
4 citations
Heliyon
Statistical Distribution Estimation and Applications
2.84
article

A novel two–parameter unit probability model with properties and applications

Abdus Saboor, Fuad A. A. Awwad, Farrukh Jamal, Musharraf Ali, Shakaiba Shafiq, Emad A. A. Ismail, Zawar Hussain, Arshid Khan, Shahida Perveen
article en
4 citations

Abstract

This paper develops a novel two-parameter unit probability model which is the generalized form Kumaraswami distribution that exhibits greater flexibility compared to well-known existing distributions, attributed to its distinct hazard and density function shapes. Extensive analysis has been conducted to explore numerous statistical features of the specified distribution, specifically moments, and order statistics providing explicit expressions for these measures. The maximum likelihood estimation is employed to estimate the model parameters and a numerical simulation analysis confirms the consistency of this estimation approach. Furthermore, the applicability of the specified model is demonstrated by considering four real data sets, showcasing its effectiveness in capturing the characteristics of real life data. The proposed model shows promise as a versatile tool for analyzing diverse data sets in a wide range of fields.

HeliyonVol. 10(18)
M.J.P. Rohilkhand University (IN), Islamia University of Bahawalpur (PK), Kohat University of Science and Technology (PK), King Saud University (SA)
King Saud University
Openalex Percentile: Top 9%
Statistical Distribution Estimation and Applications
2.84
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