Two parameter cubic rank transmutation of Lindley distribution

Sakthivel, K.M.; Rajitha, Charith; Dhivakar, K.

doi:10.1063/5.0017029

Cited by 5 publications

(1 citation statement)

References 11 publications

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“…Later, Nadarajah et al [21] introduced a new generalization of the Lindley distribution called the generalized Lindley distribution. Sakthivel et al [27] introduced two parameter cubic rank transmutation of Lindley distribution. Ashly and Rajitha [7] proposed the negative binomial improved second degree Lindley distribution.…”

Section: Introductionmentioning

confidence: 99%

Generalization of the Lindley distribution with application to COVID-19 data

Rajitha

Akhilnath

2022

Int J Data Sci Anal

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Creating new distributions with more desired and flexible qualities for modeling lifetime data has resulted in a concentrated effort to modify or generalize existing distributions. In this paper, we propose a new distribution called the power exponentiated Lindley (PEL) distribution by generalizing the Lindley distribution using the power exponentiated family of distributions, that can fit lifetime data. Then the main statistical properties such as survival function, hazard function, reverse hazard function, moments, quantile function, stochastic ordering, MRL, order statistics, etc., of the newly proposed distribution have been derived. The parameters of the distribution are estimated using the MLE method. Then, a Monte Carlo simulation study is used to check the consistency of the parameters of the PEL distribution in terms of MSE, RMSE, and bias. Finally, we implement the PEL distribution as a statistical lifetime model for the COVID-19 case fatality ratio (in %) in China and India, and the new cases of COVID-19 reported in Delhi. Then we check whether the new distribution fits the data sets better than existing well-known distributions. Different statistical measures such as the value of the log-likelihood function, K-S statistic, AIC, BIC, HQIC, and p -value are used to assess the accuracy of the model. The suggested model seems to be superior to its base model and other well-known and related models when applied to the COVID-19 data set.

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Section: Introductionmentioning

confidence: 99%

Generalization of the Lindley distribution with application to COVID-19 data

Rajitha

Akhilnath

2022

Int J Data Sci Anal

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Transmuted lower record type inverse rayleigh distribution: estimation, characterizations and applications

Tanış

2022

Ricerche mat

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This study introduces a new lifetime distribution called the transmuted lower record type inverse Rayleigh which extends the inverse Rayleigh distribution and has the potential to model the recovery times of Covid-19 patients.The new distribution is obtained using the distributions of the first two lower record statistics of the inverse Rayleigh distribution. We discuss some statistical inferences and mathematical properties of the suggested distribution. We examine some characteristics of the proposed distribution such as density shape, hazard function,moments, moment generating function, incomplete moments,Rényi entropy, order statistics, stochastic ordering. We consider five estimation methods such as maximum likelihood, least squares, weighted least squares, Anderson-Darling, Cramér-von Mises for the point estimation of the proposed distribution. Then, a comprehensive Monte Carlo simulation study is carried out to assess the risk behavior of the examined estimators. We provide two real data applications to illustrate the fitting ability of the proposed model, and compare its fit with competitor ones. Unlike many previously proposed distributions, the introduced distribution in this paper has modeled the recovery times of Covid-19 patients.

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The odd Weibull Lindley distribution for modeling wind energy data

Rajitha,

Anisha

2024

Int J Data Sci Anal

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Two parameter cubic rank transmutation of Lindley distribution

Cited by 5 publications

References 11 publications

Generalization of the Lindley distribution with application to COVID-19 data

Generalization of the Lindley distribution with application to COVID-19 data

Transmuted lower record type inverse rayleigh distribution: estimation, characterizations and applications

The odd Weibull Lindley distribution for modeling wind energy data

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