A new class of Lindley distribution: System reliability, simulation and applications

This paper presents a new probability distribution called the DUS Lindley distribution, created by applying the DUS transformation to the traditional Lindley distribution. The study provides an in-depth analysis of the distribution's statistical properties. These properties include a variety of statistical measures such as the probability density function, cumulative distribution function, failure rate, survival function, reverse hazard function, Mills ratio, mean residual life, mean past life, moments, conditional moments, characteristic function, order statistics, entropy measures, likelihood ratio test and Lorenz and Bonferroni curves. Parameter estimation is performed using several methods including weighted least squares, maximum likelihood estimation, Cramer-Von Mises estimation, least squares and Anderson-Darling estimation. The paper also explores the estimation of system reliability and evaluates the performance of maximum likelihood estimators through simulation studies across different sample sizes. Finally, the DUS Lindley distribution is applied to two real-world datasets, demonstrating a better fit than other well-known distributions.

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Publication Details

Journal
Heliyon
Published
2024-09-26
DOI
https://doi.org/10.1016/j.heliyon.2024.e38335
Citations
17
Primary Topic
Statistical Distribution Estimation and Applications
Type
article
Field-Weighted Citation Impact
12.05

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article

A new class of Lindley distribution: System reliability, simulation and applications

Aafaq A. Rather, Najwan Alsadat, Ahmed M. Gemeay, Danish Qayoom et al.
17 citations
Heliyon
Statistical Distribution Estimation and Applications
12.05
article

A new class of Lindley distribution: System reliability, simulation and applications

Aafaq A. Rather, Najwan Alsadat, Ahmed M. Gemeay, Danish Qayoom, Eslam Hussam
article en
17 citations

Abstract

This paper presents a new probability distribution called the DUS Lindley distribution, created by applying the DUS transformation to the traditional Lindley distribution. The study provides an in-depth analysis of the distribution's statistical properties. These properties include a variety of statistical measures such as the probability density function, cumulative distribution function, failure rate, survival function, reverse hazard function, Mills ratio, mean residual life, mean past life, moments, conditional moments, characteristic function, order statistics, entropy measures, likelihood ratio test and Lorenz and Bonferroni curves. Parameter estimation is performed using several methods including weighted least squares, maximum likelihood estimation, Cramer-Von Mises estimation, least squares and Anderson-Darling estimation. The paper also explores the estimation of system reliability and evaluates the performance of maximum likelihood estimators through simulation studies across different sample sizes. Finally, the DUS Lindley distribution is applied to two real-world datasets, demonstrating a better fit than other well-known distributions.

HeliyonVol. 10(19)
Tanta University (EG), Symbiosis International University (IN), King Saud University (SA), University of Business and Technology (SA), Helwan University (EG)
King Saud University
Openalex Percentile: Top 1%
Statistical Distribution Estimation and Applications
12.05
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