A Study on Modification of Kumaraswamy Distribution Using Generalized Family of Distributions

This research present new univariate continuous probability distribution based on the New Kumaraswamy-G family the new distribution, termed New Kumaraswamy-Kumaraswamy distribution (NKw-Kw). This distribution is bounded within the unit interval (0, 1) which could be employed to effectively handle large datasets that exhibit asymmetric or skewed characteristics, as well as those with non-monotonic hazard rate functions as against the classical Kumaraswamy distribution. Several properties of the NKw-Kw distribution was derived. Additionally, a Monte Carlo simulation in three different scenarios was used to assess the model parameters using maximum likelihood estimation. From the goodness of fit analysis, it shows that this model has the lowest Akaike Information Criterion (AIC) compared to existing Kumaraswamy models, and it was observed that as the sample size increases, AIC values decreases which indicate the ability of Nkw-Kw model to handled complex datasets.

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Publication Details

Journal
International Journal of Science for Global Sustainability
Published
2024-12-31
DOI
https://doi.org/10.57233/ijsgs.v10i4.731
Primary Topic
Statistical Distribution Estimation and Applications
Type
article
Field-Weighted Citation Impact
0.00
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article

A Study on Modification of Kumaraswamy Distribution Using Generalized Family of Distributions

Ahmed Audu, A. M. Ndatsu, Umar Usman, Abubakar Yusuf et al.
International Journal of Science for Global Sustainability
Statistical Distribution Estimation and Applications
article

A Study on Modification of Kumaraswamy Distribution Using Generalized Family of Distributions

Ahmed Audu, A. M. Ndatsu, Umar Usman, Abubakar Yusuf, A. Bello
article en

Abstract

This research present new univariate continuous probability distribution based on the New Kumaraswamy-G family the new distribution, termed New Kumaraswamy-Kumaraswamy distribution (NKw-Kw). This distribution is bounded within the unit interval (0, 1) which could be employed to effectively handle large datasets that exhibit asymmetric or skewed characteristics, as well as those with non-monotonic hazard rate functions as against the classical Kumaraswamy distribution. Several properties of the NKw-Kw distribution was derived. Additionally, a Monte Carlo simulation in three different scenarios was used to assess the model parameters using maximum likelihood estimation. From the goodness of fit analysis, it shows that this model has the lowest Akaike Information Criterion (AIC) compared to existing Kumaraswamy models, and it was observed that as the sample size increases, AIC values decreases which indicate the ability of Nkw-Kw model to handled complex datasets.

International Journal of Science for Global SustainabilityVol. 10(4)
Federal Medical Centre (NG), Federal University Gusau, Usmanu Danfodiyo University (NG)
Openalex Percentile: Top 72%
Statistical Distribution Estimation and Applications
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