On A New Weighted Exponential Distribution: Theory and Application

Oguntunde, Pelumi E.; Owoloko, E. A.; Balogun, Oluwafemi Samson

doi:10.3923/ajaps.2016.1.12

Cited by 10 publications

(10 citation statements)

References 10 publications

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“…The first data set given in Table 1, shows the breaking stress of carbon fibres (in Gba) reported by Cordeiro and Lemonte and the second data set is from an accelerated life test of 59 conductors. The data have been previously used by Nasir et al [9] and Oguntunde et al [10] (Table 1-6).…”

Section: Estimation With Inferencementioning

confidence: 99%

New Odd Generalized Exponential - Exponential Distribution: Its Properties and Application

Vv¹

2018

BBOAJ

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In academia, inducing a shape parameter to an exponential probability model has been an old practice and quite number of approaches for doing so were discussed in the literature. In this article, we introduce two shape parameters to the exponential probability model using an idea proposed in a recent study and this resulted in a new three-parameter probability distribution entitled New Odd Generalized Exponential-Exponential distribution. The proposed probability model can serve as a better alternative to a sister probability distribution entitled Odd Generalized Exponential-Exponential distribution developed in an earlier research using a certain probability generator. Different properties of the proposed probability model comprising moment, moment generating function, quantile function, survival function and hazard function were obtained. For illustrative purposes, two datasets were analyzed to compare the performance of our proposed probability distribution with the existing odd generalized exponential-exponential distribution; and it was observed that, the proposed model fits the two datasets better.

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Section: Estimation With Inferencementioning

confidence: 99%

New Odd Generalized Exponential - Exponential Distribution: Its Properties and Application

Vv¹

2018

BBOAJ

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“…In distribution theory, flexibility and tractability are given a great deal of preference in modeling lifetime data. The tractability of a probability distribution may be useful in theory because such distribution would be easy to work with; especially when it comes to simulation of random samples, but its flexibility could be of interest industrially, Oguntunde [1]. The implication of this is that while…”

Section: Introductionmentioning

confidence: 99%

Juchez Probability Distribution: Properties and Applications

Echebiri

Mbegbu²

2022

AJPAS

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A novel distribution called the Juchez distribution is proposed and studied. This distribution is composite of both exponential and gamma distributions. The properties and features of this distribution are studied, with empirical emphasis: on the inequality relationship within the measures of central tendency, and the coefficient of variation. The model parameter was estimated using the method of maximum likelihood, where the asymptotic and consistent properties are numerically studied as well. The flexibility of this distribution is shown, through an application to a facebook Live-Streaming and Cancer data set. This distribution shows a high efficiency when compared with other one parameter distributions.

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“…Some weighted distributions defined in the statistical literature, for example the weighted inverted exponential distribution [18], weighted Weibull distribution [19] and [25], weighted multivariate normal distribution [15], weighted inverse Weibull distribution [17], weighted three parameter Weibull distribution [2]. A two parameter weighted exponential distribution introduced [21] based on a modified weighted version of Azzalini's approach [3]. A two parameter general class of distribution based on Lindley and a compounded exponential distribution with Lindley distribution for decreasing hazard has been discussed and apply to the real data sets [6] and [7].…”

Section: Introductionmentioning

confidence: 99%

An Inferential Aptness of a Weibull Generated Distribution and Application

Singh

Das

2021

JRSS

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In this article an attempt has been made to develop a flexible single parameter continuous distribution using Weibull distribution. The Weibull distribution is most widely used lifetime distributions in both medical and engineering sectors. The exponential and Rayleigh distribution is particular case of Weibull distribution. Here in this study we use these two distributions for developing a new distribution. Important statistical properties of the proposed distribution is discussed such as moments, moment generating and characteristic function. Various entropy measures like Rényi, Shannon and cumulative entropy are also derived. The kthkt⁢h order statistics of pdf and cdf also obtained. The properties of hazard function and their limiting behavior is discussed. The maximum likelihood estimate of the parameter is obtained that is not in closed form, thus iteration procedure is used to obtain the estimate. Simulation study has been done for different sample size and MLE, MSE, Bias for the parameter λλ has been observed. Some real data sets are used to check the suitability of model over some other competent distributions for some data sets from medical and engineering science. In the tail area, the proposed model works better. Various model selection criterion such as -2LL, AIC, AICc, BIC, K-S and A-D test suggests that the proposed distribution perform better than other competent distributions and thus considered this as an alternative distribution. The proposed single parameter distribution is found more flexible as compare to some other two parameter complicated distributions for the data sets considered in the present study.

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On A New Weighted Exponential Distribution: Theory and Application

Cited by 10 publications

References 10 publications

New Odd Generalized Exponential - Exponential Distribution: Its Properties and Application

New Odd Generalized Exponential - Exponential Distribution: Its Properties and Application

Juchez Probability Distribution: Properties and Applications

An Inferential Aptness of a Weibull Generated Distribution and Application

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