The EX-Lindley distribution with applications to renewable energy sources data

This study introduces a novel extension of the X-Lindley model, called the EX-Lindley model. The EX-Lindley model is a mixture model that combines the characteristics of the gamma model with the weighted Lindley model. The EX-Lindley model is analyzed to determine various statistical properties, including ordinary moments, inverse moments, moment generating function, incomplete moments, conditional moments, mean deviation, Lorenz, mean residual life, mean inactivity time, order statistics, and extropy. The maximum likelihood method is employed to estimate model parameters using complete data. Moreover, the simulation study is used to compare and evaluate the attributes of the estimations for this generalized model. Finally, two examples of datasets on renewable energy sources are utilized to show the importance of the new model compared to some well-known statistical models.

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Publication Details

Journal
Journal of Radiation Research and Applied Sciences
Published
2024-11-06
DOI
https://doi.org/10.1016/j.jrras.2024.101161
Citations
2
Primary Topic
Statistical Distribution Estimation and Applications
Type
article
Field-Weighted Citation Impact
1.42

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article

The EX-Lindley distribution with applications to renewable energy sources data

Oluwafemi Samson Balogun, Ibrahim E. Ragab, Mohamed Kayid, Mohammed Elgarhy
2 citations
Journal of Radiation Research and Applied Sciences
Statistical Distribution Estimation and Applications
1.42
article

The EX-Lindley distribution with applications to renewable energy sources data

Oluwafemi Samson Balogun, Ibrahim E. Ragab, Mohamed Kayid, Mohammed Elgarhy
article en
2 citations

Abstract

This study introduces a novel extension of the X-Lindley model, called the EX-Lindley model. The EX-Lindley model is a mixture model that combines the characteristics of the gamma model with the weighted Lindley model. The EX-Lindley model is analyzed to determine various statistical properties, including ordinary moments, inverse moments, moment generating function, incomplete moments, conditional moments, mean deviation, Lorenz, mean residual life, mean inactivity time, order statistics, and extropy. The maximum likelihood method is employed to estimate model parameters using complete data. Moreover, the simulation study is used to compare and evaluate the attributes of the estimations for this generalized model. Finally, two examples of datasets on renewable energy sources are utilized to show the importance of the new model compared to some well-known statistical models.

Journal of Radiation Research and Applied SciencesVol. 17(4)
University of Eastern Finland (FI), King Saud University (SA), Finland University (FI), Egyptian Institute of Alexandria Academy for Administration & Accounting (EG), High Institute for Management Sciences (EG)
Deanship of Scientific Research, King Saud University
Affordable and clean energy
Openalex Percentile: Top 16%
Statistical Distribution Estimation and Applications
1.42
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