Transmuted Two-Parameter Lindley Distribution

Al-khazaleh, Moath; Al-Omari, Amer Ibrahim; Al-khazaleh, Ahmed M. H.

doi:10.18576/jsap/050306

Cited by 5 publications

(4 citation statements)

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“…The TLL 1 distribution of Kemaloglu and Yilmaz (2017) is the same as the TTL distribution of Al-Khazaleh et al (2016). The TTL distribution is numbered 34) in the present work.…”

Section: ) the CD F Of Ewpf Is Given Bymentioning

confidence: 97%

Characterizations and Infinite Divisibility of Certain Recently Introduced Distributions IV

Hamedani¹

2018

IJSP

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“…The TLL 1 distribution of Kemaloglu and Yilmaz (2017) is the same as the TTL distribution of Al-Khazaleh et al (2016). The TTL distribution is numbered 34) in the present work.…”

Section: ) the CD F Of Ewpf Is Given Bymentioning

confidence: 97%

Characterizations and Infinite Divisibility of Certain Recently Introduced Distributions IV

Hamedani¹

2018

IJSP

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“…Extended Power Lindley distribution by [12], Transmuted Two-Parameter Lindley distribution by [13] and a three-parameter Lindley distribution by [14], power Lindley distribution by [15], the transmuted Lindley-geometric distribution by [16], the beta-Lindley distribution by [17], Kumaraswamy-Lindley distribution by [18] and Gompertz-Lindley distribution by [19].…”

Section: Introductionmentioning

confidence: 99%

Estimation of a Shape Parameter of a Gompertz-lindley Distribution Using Bayesian and Maximum Likelihood Methods

Eraikhuemen,

Asongo,

Umar

et al. 2023

JSRR

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The Gompertz-Lindley distribution is an extension of the Lindley distribution with three parameters. It was found to be more flexible for modeling real life events. The distribution contains two shape parameters and a scale parameter. Despite the necessity of parameter estimation theory in modeling, it has not been shown that a method of estimation method is better for any of these three parameters of the Gompertz-Lindley distribution. This paper identifies the best estimation method for the shape parameter of the Gompertz-Lindley distribution by deriving Bayesian estimators for the shape parameter of the distribution using two non-informative prior distributions (Uniform and Jeffery) and an informative prior (gamma) under squared error loss function (SELF), quadratic loss function (QLF) and precautionary loss function (PLF). These estimators were evaluated and the results compared with the maximum likelihood estimation method using Monte Carlo simulations with the mean square error (MSE) as a criterion for choosing the best estimator.

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“…The quest to improve on distributions by introducing higher parameter distributions is widespread in the field of probability. Scholars [2,3,4,5] improved upon one distribution or another through parameterization.…”

Section: Introductionmentioning

confidence: 99%

Kumaraswamy-Janardan Distribution: A Generalized Janardan Distribution with Application to Real Data

Dzivor

Otoo

Wiah

2021

AJPAS

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The quest to improve on flexibility of probability distributions motivated this research. Four-parameter Janardan generalized distribution known as Kumaraswamy-Janardan distribution is proposed through method of parameterization and studied. The probability density function, cumulative density function, survival rate function as well as hazard rate function of the distribution are established. Statistical properties such as moments, moment generating function as well as maximum likelihood of the model are discussed. The parameters are estimated using the simulated annealing optimization algorithm. Flexibility of the model in comparison with the baseline model as well as other competing sub-models is verified using Akaike Information Criteria (AIC). The model is tested with real data and is proven to be more flexible in fitting real data than any of its sub-models considered.

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Transmuted Two-Parameter Lindley Distribution

Cited by 5 publications

References 0 publications

Characterizations and Infinite Divisibility of Certain Recently Introduced Distributions IV

Characterizations and Infinite Divisibility of Certain Recently Introduced Distributions IV

Estimation of a Shape Parameter of a Gompertz-lindley Distribution Using Bayesian and Maximum Likelihood Methods

Kumaraswamy-Janardan Distribution: A Generalized Janardan Distribution with Application to Real Data

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