Parameter Estimation In Weighted Rayleigh Distribution

Ajami, Masoud; Jahanshahi, Seyed Mahdi Amir

doi:10.22237/jmasm/1509495240

Cited by 16 publications

(11 citation statements)

References 10 publications

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“…Several models have been investigated in last few years and a number of symmetric and asymmetric loss functions have been shown to be functional; we refer readers to (Nassir & Ibrahim, 2020), (Ramos, Louzada, Ramos, & Dey, 2019), (Reshi, Ahmad, & Ahmad, 2019), (Dar, Ahmed, & Reshi, 2018), (Ajami & Jahanshahi, 2017), (Oluwafemi, 2017), (Mudasir Sofi, Ahamad, S.P., Ahamad, A., 2016), (Ahmad, Ahmad, & Ahmed, 2016), (Tahir, 2015), (Reshi, J.A., Ahmad, A. and Mir, K.A., 2014), (Alzaatreh, Famoye, & Lee, 2013).…”

Section: B Methodsmentioning

confidence: 99%

Bayes Estimation of Shape Parameter of Length Biased Weibull Distribution

Rao

Pandey

2021

JTAM

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Section: B Methodsmentioning

confidence: 99%

Bayes Estimation of Shape Parameter of Length Biased Weibull Distribution

Rao

Pandey

2021

JTAM

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“…Saghirand Saleem [17] presented a new version of the double weight Inverse Weibull (DWIW) distribution. Ajami and Jahanshahi [21] introduced a weighted model based on the Rayleigh distribution and they derived and studied its statistical and reliability properties. Asgharzadeh, et al [22] introduced a generalization of Lindley distribution that provides fits for the Lindley distribution and some other distributions as special cases.…”

Section: Introductionmentioning

confidence: 99%

Log-Weighted Pareto Distribution And Its Statistical Properties

Mandouh¹,

Mohamed²

2021

Journal of Data Science

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The Pareto distribution is a power law probability distribution that is used to describe social scientific, geophysical, actuarial, and many other types of observable phenomena. A new weighted Pareto distribution is proposed using a logarithmic weight function. Several statistical properties of the weighted Pareto distribution are studied and derived including cumulative distribution function, location measures such as mode, median and mean, reliability measures such as reliability function, hazard and reversed hazard functions and the mean residual life, moments, shape indices such as skewness and kurtosis coefficients and order statistics. A parametric estimation is performed to obtain estimators for the distribution parameters using three different estimation methods the maximum likelihood method, the L-moments method and the method of moments. Numerical simulation is carried out to validate the robustness of the proposed distribution. The distribution is fitted to a real data set to show its importance in real life applications.

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“…Xu et al [15] considered the Bayesian inference for the loss and risk functions of parameter of Rayleigh distribution. Ajami and Jahanshahi [16] studied parameter estimation of Weighted Rayleigh Distribution in the classical as well as in Bayesian framework. Teena et al [19,20].…”

Section: Introductionmentioning

confidence: 99%

On Bayesian Estimation of Loss and Risk Functions

Singh¹

2021

SJAMS

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Loss functions and Risk functions play very important role in Bayesian estimation. This paper aims at the Bayesian estimation for the loss and risk functions of the unknown parameter of the H(r, theta), (theta being the unknown parameter) distribution The estimation has been performed under Rukhin's loss function. The importance of this distribution is that it contains some important distributions such as the Half Normal distribution, Rayleigh distribution and Maxwell's distribution as particular cases. The inverse Gamma distribution has assumed as the prior distribution for the unknown parameter theta. This prior distribution is a Natural Conjugate prior distribution for the unknown parameter because the posterior probability density function of the unknown parameter is also inverse gamma distribution The Rukhin's loss function involves another loss function denoted by w(theta, delta) he form of w(theta, delta) is important as it changes the estimate. In this paper, three forms of w(theta, delta) have been taken and corresponding estimates have been derived. The three, forms are, the Squared Error Loss Function (SELF) and two different forms of Weighted Squared Error Loss Function (WSELF) namely, the Minimum Expected Loss (MELO) Function and the Exponentially Weighted Minimum Expected Loss (EWMELO) Function have been considered. A criterion of performance of various form of w(theta, delta) has ben defined. It has been proved that among three forms of w(theta, delta), considered here, the form corresponding to EWMELO is most dominant.

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Parameter Estimation In Weighted Rayleigh Distribution

Cited by 16 publications

References 10 publications

Bayes Estimation of Shape Parameter of Length Biased Weibull Distribution

Bayes Estimation of Shape Parameter of Length Biased Weibull Distribution

Log-Weighted Pareto Distribution And Its Statistical Properties

On Bayesian Estimation of Loss and Risk Functions

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