Improved confidence intervals for the scale parameter of Burr XII model based on record values

Belaghi, Reza Arabi; Arashi, Mohammad; Tabatabaey, S. M. M.

doi:10.1007/s00180-014-0484-3

Cited by 12 publications

(7 citation statements)

References 43 publications

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“…The value of k near to zero (one) implies a strong belief in the guess value θ 0 (sample values). Significant attention has been paid to the problem of shrinkage estimation, see Prakash and Singh (2008), Naghizadeh Qomi and Barmoodeh (2015), Belaghi et al (2014), Belaghi et al (2015 a, b), Kiapour and Naghizadeh (2016), Baklizi et al (2016), Naghizadeh Qomi (2017a), Safarian et al (2018) and Volterma et al (2018).…”

Section: Introductionmentioning

confidence: 99%

Performance of a Class of Bayes Shrinkage Estimators Based on Rayleigh Record Data under Reflected Gamma Loss Function

Qomi

Dey²,

Fathollahi

2019

JIRSS

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This article addresses the problem of Bayesian shrinkage estimation for the Rayleigh scale parameter based on record values under the reflected gamma loss (RGL) function. A class of Bayesian shrinkage estimators using prior point information is constructed. The risk functions of the maximum likelihood estimator (MLE) and proposed Bayesian shrinkage estimator are derived under the RGL function. The performance of Bayesian shrinkage estimator is compared with the MLE numerically and graphically. One data set has been analyzed to illustrate the performance of the Bayesian shrinkage estimator.

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Section: Introductionmentioning

confidence: 99%

Performance of a Class of Bayes Shrinkage Estimators Based on Rayleigh Record Data under Reflected Gamma Loss Function

Qomi

Dey²,

Fathollahi

2019

JIRSS

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“…One can construct shrinkage preliminary test estimators for the parameter θ based on the acceptance or rejection of H 0 . Pandey and Singh (1980), Prakash and Singh (2008), Kibria et al (2010), Ahmed et al (2012), Mirfarah and Ahmadi (2014), Arabi Belaghi et al (2014, 2015a, Naghizadeh Qomi and Barmoodeh (2015) and Hossain and Howlader (2016) considered the problem of shrinkage estimation. The aim of this paper is constructing shrinkage preliminary test estimators in exponential distribution under a precautionary loss function.…”

Section: Introductionmentioning

confidence: 99%

Shrinkage preliminary test estimation under a precautionary loss function with applications on records and censored ‎data

Kiapour

Qomi

2016

JIRSS

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Abstract. Shrinkage preliminary test estimation in exponential distribution under a precautionary loss function is considered. The minimum risk-unbiased estimator is derived and some shrinkage preliminary test estimators are proposed. We apply our results on censored data and records. The relative efficiencies of proposed estimators with respect to the minimum risk-unbiased estimator based on record data under the considered loss function are computed for evaluating the performance of these estimators.

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“…Burr distribution contains twelve forms, which first introduced by Burr in 1942 [1]. However, Burr type XII distribution is the most of the authors have considered [2][3][4][5][6], But other types of Burr distribution do not get much attention. Feroze and Aslam [7] studied the Bayes estimation of the parameter of Burr type XI distribution under the assumption of eight priors (informative, non-informative, single and mixture of priors).…”

Section: Introductionmentioning

confidence: 99%

“…For example, Wang et al [14] investigated the point estimation and confidence intervals estimation for the parameter of Weibull distribution based on record values. Arabi Belaghi et al [15] considered several different types confidence intervals for the scale parameter of Burr type XII distribution based on upper record values. They put forward improved confidence intervals estimation based on the preliminary test technique, Thompson shrinkage technique and Bayesian approach.…”

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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Improved confidence intervals for the scale parameter of Burr XII model based on record values

Cited by 12 publications

References 43 publications

Performance of a Class of Bayes Shrinkage Estimators Based on Rayleigh Record Data under Reflected Gamma Loss Function

Performance of a Class of Bayes Shrinkage Estimators Based on Rayleigh Record Data under Reflected Gamma Loss Function

Shrinkage preliminary test estimation under a precautionary loss function with applications on records and censored ‎data

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

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