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GENERALIZED WEIGHTED SUJATHA DISTRIBUTION WITH PROPERTIES AND APPLICATIONS.

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Title: GENERALIZED WEIGHTED SUJATHA DISTRIBUTION WITH PROPERTIES AND APPLICATIONS.
Authors: Prodhani, Hosenur Rahman1 hosenur72@gmail.com, Shanker, Rama1 shankerrama2009@gmail.com
Source: Reliability: Theory & Applications. Mar2026, Vol. 21 Issue 1, p109-123. 15p.
Subjects: Distribution (Probability theory), Hazard function (Statistics), Descriptive statistics, Goodness-of-fit tests, Survival analysis (Biometry), Maximum likelihood statistics, Bayes' estimation, Failure time data analysis
Abstract: This paper introduces three-parameter weighted generalized Sujatha distribution which is the weighted version of the generalization of Sujatha distribution to model over-dispersed data from engineering and medical science. The proposed model retains mathematical tractability and includes Lindley distribution, Sujatha distribution, generalized Sujatha distribution, weighted Sujatha distribution, weighted Lindley distribution, size-biased Sujatha distribution and size-biased Lindley distribution as special cases. The statistical properties including moments and its related measures such as coefficient of variation, coefficient of skewness, coefficient of kurtosis and index of dispersion have studied. The survival function, hazard function, reverse hazard function and mean residual life function of the distribution have also been studied. The parameters of the distribution have been estimated by maximum likelihood estimation method and Bayesian estimation method and a simulation study has been conducted using acceptance-rejection method of simulation to know the consistency of the estimator of the parameters. Bootstrap confidence interval has been used for interval estimation of the parameters. To validate the applicability of the distribution, two real lifetime datasets from medical and engineering are analyzed. The goodness of fit of the generalized weighted Sujatha distribution is evaluated using the Akaike Information criterion Bayesian Information Criterion, Consistent Akaike Information Criterion, Hannan-Quinn Information Criterion and Kolmogorov- Smirnov statistic. The results demonstrate that the proposed distribution offers closer fit compared to threeparameter weighted Lindley distribution, weighted quasi Akash distribution, weighted quasi Shanker distribution, weighted quasi Aradhana distribution, three-parameter Sujatha distribution, three-parameter generalized Lindley distribution, generalized Sujatha distribution and Sujatha distribution. It has been found that the proposed distribution provides much closer fit as compared to the considered distributions. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:This paper introduces three-parameter weighted generalized Sujatha distribution which is the weighted version of the generalization of Sujatha distribution to model over-dispersed data from engineering and medical science. The proposed model retains mathematical tractability and includes Lindley distribution, Sujatha distribution, generalized Sujatha distribution, weighted Sujatha distribution, weighted Lindley distribution, size-biased Sujatha distribution and size-biased Lindley distribution as special cases. The statistical properties including moments and its related measures such as coefficient of variation, coefficient of skewness, coefficient of kurtosis and index of dispersion have studied. The survival function, hazard function, reverse hazard function and mean residual life function of the distribution have also been studied. The parameters of the distribution have been estimated by maximum likelihood estimation method and Bayesian estimation method and a simulation study has been conducted using acceptance-rejection method of simulation to know the consistency of the estimator of the parameters. Bootstrap confidence interval has been used for interval estimation of the parameters. To validate the applicability of the distribution, two real lifetime datasets from medical and engineering are analyzed. The goodness of fit of the generalized weighted Sujatha distribution is evaluated using the Akaike Information criterion Bayesian Information Criterion, Consistent Akaike Information Criterion, Hannan-Quinn Information Criterion and Kolmogorov- Smirnov statistic. The results demonstrate that the proposed distribution offers closer fit compared to threeparameter weighted Lindley distribution, weighted quasi Akash distribution, weighted quasi Shanker distribution, weighted quasi Aradhana distribution, three-parameter Sujatha distribution, three-parameter generalized Lindley distribution, generalized Sujatha distribution and Sujatha distribution. It has been found that the proposed distribution provides much closer fit as compared to the considered distributions. [ABSTRACT FROM AUTHOR]
ISSN:19322321
DOI:10.24412/1932-2321-2026-190-109-123