Bayesian Estimation and Prediction of Burr Type XI Distribution under Singly and Doubly Censored Samples

Feroze, Navid; Aslam, Muhammad; Saleem, Azhar

doi:10.14257/ijhit.2014.7.2.29

Cited by 6 publications

(8 citation statements)

References 14 publications

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“…In recent years, development focuses on new techniques for building meaningful distributions, which include the use of the logit of beta (the link function of the beta generalized distribution which was introduced by Jones [14] and elsewhere [10]). The logit of beta introduced by Jones has probability density function (pdf).…”

Section: Beta-burr Type V (Bbv)mentioning

confidence: 99%

The beta-burr type v distribution: its properties and application to real life data

Dikko

Aliyu

Alfa

2017

IJASP

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A new distribution called the beta-Burr type V distribution that extends the Burr type V distribution was defined, investigated and established. The properties examined provide a comprehensive mathematical treatment of the distribution. Additionally, various structural properties of the new distribution verified include probability density function verification, asymptotic behavior, Hazard Rate Function and the cumulative distribution. Subsequently, we used the maximum likelihood estimation procedure to estimate the parameters of the new distribution. Application of real data set indicates that this new distribution would serve as a good alternative distribution function to model real-life data in many areas.

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Section: Beta-burr Type V (Bbv)mentioning

confidence: 99%

The beta-burr type v distribution: its properties and application to real life data

Dikko

Aliyu

Alfa

2017

IJASP

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“…The entropy and precautionary loss functions were used in their Bayesian analysis. Feroze et al [8] studied the Bayes estimation and prediction of the Burr type XI distribution based on censored samples on the basis of Five informative and non-informative priors under five different (symmetric and asymmetric) loss functions for posterior analysis.…”

Section: Introductionmentioning

confidence: 99%

Admissibility Estimation of Burr Type XI Distribution Under Entropy Loss Function Based on Record Values

Li¹

2016

AJTAS

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The aim of this paper is to study the estimation of parameter of Burr Type XI distribution on the basis of lower record values. First, the minimum variance unbiased estimator and maximum likelihood estimator are obtained. Then the Bayes and empirical Bayes estimators of the unknown parameter are derived under entropy loss function. Finally, the admissibility and inadmissibility of a class of inverse linear estimators are discussed.

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“…This property have motivated the authors to use this distribution for modeling extreme events such as flood frequencies, wind speeds, rainfalls and river discharge volumes. The details regarding these contributions can be seen from: Soliman (2002) Nadar and Alexandros (2011) and Feroze and Aslam (2012a). From other members of this family of distributions, the Bayesian analysis of Burr type VII and XI distributions have been discussed by Feroze and Aslam (2012b) and Feroze and Aslam (2012c) respectively.…”

Section: Introductionmentioning

confidence: 99%

“…A simple fixed point iteration algorithm can be used to solve this optimization problem. Firstly, the parameter  in log-likelihood (4) has been replaced by its MLE given in (7) the resultant log-likelihood becomes: After some simplifications it can be presented as:…”

Section: Introductionmentioning

confidence: 99%

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On Maximum Likelihood Estimation for Left Censored Burr Type III Distribution

Feroze

Aslam²,

Sindhu³

2015

Pak.j.stat.oper.res.

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Burr type III is an important distribution used to model the failure time data. The paper addresses the problem of estimation of parameters of the Burr type III distribution based on maximum likelihood estimation (MLE) when the samples are left censored. As the closed form expression for the MLEs of the parameters cannot be derived, the approximate solutions have been obtained through iterative procedures. An extensive simulation study has been carried out to investigate the performance of the estimators with respect to sample size, censoring rate and true parametric values. A real life example has also been presented. The study revealed that the proposed estimators are consistent and capable of providing efficient results under small to moderate samples. Pak.j.stat.oper.res. Vol.XI No.4 2015 pp497-512 498 Materials and MethodsThis section contains the derivation of maximum likelihood estimates, elements of Fisher information matrix, variance covariance matrix and confidence intervals for the parameters of the Burr type iii distribution under left censored samples. The limiting behavior of the Fisher information matrix has also been discussed. Maximum likelihood estimationBased on the left censored sample the likelihood function along with maximum likelihood estimators of the parameters of the Burr type iii distribution have been On Maximum Likelihood Estimation for Left Censored Burr Type III Distribution Pak.j.stat.oper.res. Vol.XI No.4 2015 pp497-512 499 discussed in the following. Let     1 ... rn XX  be the last nr  ordered statistics from the Burr type iii distribution. Then, the likelihood function for the sample of nr  left censored sample can be defined as:

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Bayesian Estimation and Prediction of Burr Type XI Distribution under Singly and Doubly Censored Samples

Abstract: Abstract

Cited by 6 publications

References 14 publications

The beta-burr type v distribution: its properties and application to real life data

The beta-burr type v distribution: its properties and application to real life data

Admissibility Estimation of Burr Type XI Distribution Under Entropy Loss Function Based on Record Values

On Maximum Likelihood Estimation for Left Censored Burr Type III Distribution

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