Slashed generalized Rayleigh distribution

Iriarte, Yuri A.; Vilca, Filidor; Varela, Héctor; Gómez, Héctor W.

doi:10.1080/03610926.2015.1066811

Cited by 22 publications

(20 citation statements)

References 14 publications

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“…2β , 1 and θ = (α, β, q). The ML estimators are obtained by maximizing the log-likelihood function given in (8). By deriving the log-likelihood function with respect to each parameter, the following estimating equations are obtained: (1) are the digamma function.…”

Section: Estimationmentioning

confidence: 99%

“…Reference Gómez et al [7] used this family to extend the Birnbaum-Saunders distribution. Reference Iriarte et al [8] and Gómez et al [9] used this methodology to extend the generalized Rayleigh distribution and Gumbel respectively. Reference Olmos et al [10] also used this methodology to introduce the modified slashed half-normal distribution.…”

Section: Introductionmentioning

confidence: 99%

“…If β = 1 then Z = Y with α = 1 2 σ, where Y has slash Maxwell (SM) distribution (Iriarte et al [8]).…”

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confidence: 99%

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A Power Maxwell Distribution with Heavy Tails and Applications

et al. 2020

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In this paper we introduce a distribution which is an extension of the power Maxwell distribution. This new distribution is constructed based on the quotient of two independent random variables, the distributions of which are the power Maxwell distribution and a function of the uniform distribution (0,1) respectively. Thus the result is a distribution with greater kurtosis than the power Maxwell. We study the general density of this distribution, and some properties, moments, asymmetry and kurtosis coefficients. Maximum likelihood and moments estimators are studied. We also develop the expectation–maximization algorithm to make a simulation study and present two applications to real data.

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Section: Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Power Maxwell Distribution with Heavy Tails and Applications

et al. 2020

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“…In the past few years, different generalizations of Rayleigh distribution have been introduced to model the life time data. Merovci (2013) developed the transmuted Rayleigh distribution, Sindhu et al (2013) considered the Bayesian estimation of inverse Rayleigh distribution using left censored data, Gomes et al (2014) introduced the Kumaraswamy generalized Rayleigh distribution for modeling life time data, Merovci and Elbatal (2015) defined and studied the three parameter Weibull Rayleigh distribution, Haq (2016) introduced Kumaraswamy exponentiated inverse Rayleigh distribution, Iriarte et al (2017) introduced the slashed generalized Rayleigh distribution, Ajami and Jahanshahi (2017) proposed weighted version Rayleigh distribution and estimate the parameter of the said model under Bayesian and Non-Bayesian methods of estimation, Bashir and Rasul (2018) introduced the area-biased Rayleigh distribution and estimate its parameter by using different methods of estimation. Sofi et al (2019) studied the weighted version of Rayleigh distribution and showed that the generalized model gives a best fit to real life data sets as compared to its sub models.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study for Weighted Rayleigh Distribution

Ahad¹,

Ahmad²,

Rehman³

2021

JRSS

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In this paper, Bayesian and non-Bayesian methods are used for parameter estimation of weighted Rayleigh (WR) distribution. Posterior distributions are derived under the assumption of informative and non-informative priors. The Bayes estimators and associated risks are obtained under different symmetric and asymmetric loss functions. Results are compared on the basis of posterior risk and mean square error using simulated and real life data sets. The study depicts that in order to estimate the scale parameter of the weighted Rayleigh distribution use of entropy loss function under Gumbel type II prior can be preferred. Also, Bayesian method of estimation having least values of mean squared error gives better results as compared to maximum likelihood method of estimation.

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“…In fact, this is time-between-events distribution that is used to characterize the point process. There are many studies that extend the existing probability models, see, for instance, [6,9,11,12,14,16,17,19,21,26,29], and references cited therein.…”

Section: Introductionmentioning

confidence: 99%

Bivariate discrete Nadarajah and Haghighi distribution: Properties and different methods of estimation

Ali

Shafqat

Shah

et al. 2019

Filomat

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The exponential distribution is commonly used to model different phenomena in statistics and reliability engineering. A new extension of exponential distribution known as the Nadarajah and Haghighi [An extension of the exponential distribution, Statistics: A Journal of Theoretical and Applied Statistics 45 (2011) 543-558.] distribution was introduced in the literature to accommodate the inflation of zero in the data. In practice, however, discrete data are easy to collect as compared to continuous data. Discrete bivariate distributions play important roles in modeling bivariate lifetime count data. Thus focusing on the utility of discrete data, this study presents a new bivariate discrete Nadarajah and Haghighi distribution. We discuss some basic properties of the proposed distribution and study seven different methods of estimation for the unknown parameters to assess the performance of the proposed bivariate discrete model. Two data sets are also analyzed to demonstrate how the proposed model may work in practice. Results show that the proposed model is very flexible and performs better than some of the existing models.

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Slashed generalized Rayleigh distribution

Cited by 22 publications

References 14 publications

A Power Maxwell Distribution with Heavy Tails and Applications

A Power Maxwell Distribution with Heavy Tails and Applications

A Comparative Study for Weighted Rayleigh Distribution

Bivariate discrete Nadarajah and Haghighi distribution: Properties and different methods of estimation

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