The Harris Extended Exponential Distribution

Pinho, Luís Gustavo Bastos; Cordeiro, Gauss M.; Nobre, Juvêncio S.

doi:10.1080/03610926.2013.851221

Cited by 40 publications

(23 citation statements)

References 15 publications

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“…Thus, the MLEs of the parameters c, β, k, and s can be obtained by setting Equations (24)- (27) to zero and solving them iteratively, using numerical methods such as the Newton-Raphson iteration method. Alternatively, the log-likelihood in Equation (23) can be directly maximized, using any standard non-linear optimization tool.…”

Section: Parameter Estimation For Weibull-gamma Distributionmentioning

confidence: 99%

“…The dataset considered here, discussed by [27], presents the waiting times between 65 consecutive eruptions of a blowhole, called the Kiama Blowhole, as follows: 83, 51, 87, 60, 28, 95, 8, 27, 15, 10, 18, 16, 29, 54, 91, 8, 17, 55, 10, 35,47, 77, 36, 17, 21, 36, 18, 40 , 10, 7, 34, 27, 28, 56, 8, 25, 68, 146, 89, 18, 73, 69, 9, 37, 10, 82, 29, 8, 60, 61, 61, 18, 169, 25, 8, 26, 11, 83, 11, 42, 17, 14, 9, and 12.…”

Section: Fourth Datasetmentioning

confidence: 99%

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The Weibull-Gamma Distribution: Properties and Applications

Klakattawi

2019

Entropy

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A new member of the Weibull-generated (Weibull-G) family of distributions—namely the Weibull-gamma distribution—is proposed. This four-parameter distribution can provide great flexibility in modeling different data distribution shapes. Some special cases of the Weibull-gamma distribution are considered. Several properties of the new distribution are studied. The maximum likelihood method is applied to obtain an estimation of the parameters of the Weibull-gamma distribution. The usefulness of the proposed distribution is examined by means of five applications to real datasets.

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Section: Parameter Estimation For Weibull-gamma Distributionmentioning

confidence: 99%

Section: Fourth Datasetmentioning

confidence: 99%

The Weibull-Gamma Distribution: Properties and Applications

Klakattawi

2019

Entropy

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“…Recently, many authors have developed several generalizations of the exponential distribution to increase its flexibility. For example, the Marshall-Olkin exponential by Marshall and Olkin [1], exponentiated exponential by Gupta and Kundu [2], Harris extended exponential by Pinho et al [3], Kumaraswamy transmuted exponential by Afify et al [4], modified exponential by Rasekhi et al [5], odd exponentiated half-logistic exponential by Afify et al [6], Marshall-Olkin logistic-exponential by Mansoor et al [7], odd log-logistic Lindley exponential by Alizadeh et al [8], and Marshall-Olkin alpha power exponential by Nassar et al [9], among others.…”

Section: Introductionmentioning

confidence: 99%

A New Three-Parameter Exponential Distribution with Variable Shapes for the Hazard Rate: Estimation and Applications

Afify

Mohamed

2020

Mathematics

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In this paper, we study a new flexible three-parameter exponential distribution called the extended odd Weibull exponential distribution, which can have constant, decreasing, increasing, bathtub, upside-down bathtub and reversed-J shaped hazard rates, and right-skewed, left-skewed, symmetrical, and reversed-J shaped densities. Some mathematical properties of the proposed distribution are derived. The model parameters are estimated via eight frequentist estimation methods called, the maximum likelihood estimators, least squares and weighted least-squares estimators, maximum product of spacing estimators, Cramér-von Mises estimators, percentiles estimators, and Anderson-Darling and right-tail Anderson-Darling estimators. Extensive simulations are conducted to compare the performance of these estimation methods for small and large samples. Four practical data sets from the fields of medicine, engineering, and reliability are analyzed, proving the usefulness and flexibility of the proposed distribution.

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“…The Floyd River data were reported by Mudholkar and Hutson [15] and Akinsete et al [2]. The second data set consists of the waiting times between 65 consecutive eruptions of the Kiama Blowhole (da Silva et al [10]; Pinho et al [17]). The Kiama Blowhole is a tourist attraction located nearly 120km to the south of Sydney.…”

Section: Applicationsmentioning

confidence: 99%

The Gamma Half-Cauchy Distribution: Properties and Applications

Alzaatreh¹,

Mansoory²,

Tahirz³

et al. 2015

HJMS

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A new distribution, namely, the Gamma-Half-Cauchy distribution is proposed. Various properties of the Gamma-Half-Cauchy distribution are studied in detail such as limiting behavior, moments, mean deviations and Shannon entropy. The model parameters are estimated by the method of maximum likelihood and the observed information matrix is obtained. Two data sets are used to illustrate the applications of Gamma-Half-Cauchy distribution.

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The Harris Extended Exponential Distribution

Cited by 40 publications

References 15 publications

The Weibull-Gamma Distribution: Properties and Applications

The Weibull-Gamma Distribution: Properties and Applications

A New Three-Parameter Exponential Distribution with Variable Shapes for the Hazard Rate: Estimation and Applications

The Gamma Half-Cauchy Distribution: Properties and Applications

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