A Lomax-inverse Lindley Distribution: Model, Properties and Applications to Lifetime Data

Ieren, Terna Godfrey; Koleoso, Peter O.; Chama, Adana’a Felix; Eraikhuemen, Innocent Boyle; Yakubu, Nasiru

doi:10.9734/jamcs/2019/v34i3-430208

Cited by 13 publications

(12 citation statements)

References 17 publications

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“…Besides these generalized Rayleigh distributions, some researchers have proven that most extended or compound distributions are more flexible and perform better than their standard counterparts when applied to real life datasets. For instance, the Weibull-Exponential distribution was found to perform better than the Exponential distribution (Oguntunde et al [12]), the Weibull-Frechet distribution exhibited a very higher level of flexibility when applied to real life data compared to the standard Frechet distribution (Afify et al [13]), the Lomax-Exponential distribution was also discovered to have perform better when compared to the exponential distribution during real life data analysis (Ieren and Kuhe, [14]), others are the Weibull-Lindley distribution by Ieren et al [15], the Gompertz-Lindley distribution by Koleoso et al [16], the Lomax-inverse Lindley distribution by Ieren et al [17], the transmuted Lindley-Exponential distribution by Umar et al [18], the Power Gompertz distribution by Ieren et al [19] and many others.…”

Section: Introductionmentioning

confidence: 99%

Odd Lindley-Rayleigh Distribution: Its Properties and Applications to Simulated and Real Life Datasets

Ieren

Abdulkadir

Issa

2020

JAMCS

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This article develops an extension of the Rayleigh distribution with two parameters and greater flexibility which is an improvement over Lindley distribution, Rayleigh distribution and other generalizations of the Rayleigh distribution. The new model is known as “odd Lindley-Rayleigh Distribution”. The definitions of its probability density function and cumulative distribution function using the odd Lindley-G family of distributions are provided. Some properties of the new distribution are also derived and studied in this article with applications and discussions. The estimation of the unknown parameters of the proposed distribution is also carried out using the method of maximum likelihood. The performance of the proposed probability distribution is compared to some other generalizations of the Rayleigh distribution using three simulated datasets and a real life dataset. The results obtained are compared using the values of some information criteria evaluated with the parameter estimates of the fitted distributions based on the four datasets and it is revealed that the proposed distribution outperforms all the other fitted distributions. This performance has shown that the odd Lindley-G family of distribution is an adequate generator of probability models and that the odd Linley-Rayleigh distribution is a very flexible distribution for fitting different kinds of datasets better than the other generalizations of the Rayleigh distribution considered in this study.

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Section: Introductionmentioning

confidence: 99%

Odd Lindley-Rayleigh Distribution: Its Properties and Applications to Simulated and Real Life Datasets

Ieren

Abdulkadir

Issa

2020

JAMCS

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“…There are several standard probability distributions that have been used over the years for modelling real-life datasets however research has shown that most of these distributions do not adequately model some of these heavily skewed datasets and therefore creating a problem in statistical theory and applications. Recently, numerous extended or compound probability distributions have proposed in the literature for modeling real-life situations and these compound distributions are found to be skewed, flexible and more better in statistical modeling compared to their standard counterparts [1][2][3][4][5][6][7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Bayesian Analysis of Weibull-Lindley Distribution Using Different Loss Functions

Eraikhuemen

Bamigbala

Magaji

et al. 2020

AJARR

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In the present paper, a three-parameter Weibull-Lindley distribution is considered for Bayesian analysis. The estimation of a shape parameter of Weibull-Lindley distribution is obtained with the help of both the classical and Bayesian methods. Bayesian estimators are obtained by using Jeffrey's prior, uniform prior and Gamma prior under square error loss function, quadratic loss function and Precautionary loss function. Estimation by the method of Maximum likelihood is also discussed. These methods are compared by using mean square error through simulation study with varying parameter values and sample sizes. AJARR, 8(4): 28-41, 2020; Article no.AJARR.54833 29 Original Research Article

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“…Recently, numerous extended or compound probability distributions have been proposed in the literature for modeling real life situations. These compound distributions are found to be skewed, flexible and much better in statistical modeling compared to their standard counterparts [1][2][3][4][5][6][7][8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Bayesian and Maximum Likelihood Estimation of the Shape Parameter of Exponential Inverse Exponential Distribution: A Comparative Approach

Eraikhuemen

Mohammed

Sule

2020

AJPAS

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This paper aims at making Bayesian analysis on the shape parameter of the exponential inverse exponential distribution using informative and non-informative priors. Bayesian estimation was carried out through a Monte Carlo study under 10,000 replications. To assess the effects of the assumed prior distributions and loss function on the Bayesian estimators, the mean square error has been used as a criterion. Overall, simulation results indicate that Bayesian estimation under QLF outperforms the maximum likelihood estimation and Bayesian estimation under alternative loss functions irrespective of the nature of the prior and the sample size. Also, for large sample sizes, all methods perform equally well.

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A Lomax-inverse Lindley Distribution: Model, Properties and Applications to Lifetime Data

Cited by 13 publications

References 17 publications

Odd Lindley-Rayleigh Distribution: Its Properties and Applications to Simulated and Real Life Datasets

Odd Lindley-Rayleigh Distribution: Its Properties and Applications to Simulated and Real Life Datasets

Bayesian Analysis of Weibull-Lindley Distribution Using Different Loss Functions

Bayesian and Maximum Likelihood Estimation of the Shape Parameter of Exponential Inverse Exponential Distribution: A Comparative Approach

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