Statistical inference for generalized Pareto distribution based on progressive Type-II censored data with random removals

Azimi, Reza; Fasihi, Bahman; Sarikhanbaglu, Faramarz Azimi

doi:10.14419/ijsw.v2i1.1780

Cited by 6 publications

(4 citation statements)

References 12 publications

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“…Singh et al (2013) estimates the parameters of exponentiated Pareto distribution under random removals scheme. Azimi et al (2014) presents some statistical inference for the generalized Pareto distribution based on progressive Type-II censored data with random removals.…”

Section: Progressive Censoring With Random Schemementioning

confidence: 99%

Progressive Censored Burr Type-XII Distribution Under Random Removal Scheme: Some Inferences

Prakash¹

2017

JAS

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Abstract. When some sample values at either or both the extremes might have been assorted, a censoring scheme is much useful. Nevertheless, in some reliability experiments, the number of items fell out the experiment cannot be prefixed random in some situations. For such situations, a random removal scheme with censoring scheme may offer a good result. Here, a random removal scheme with the Progressive censoring plan is assumed for statistical inference, when fill out items of the experiments cannot be prefixed. The analysis of the present discussion has carried out with the help of a real data set for the Burr Type-XII distribution.Résumé. Lorsque certaines valeurs d'échantillonà l'une ou l'autre des deux extrêmes ont puêtre assorties, un schéma de censure est très utile. Néanmoins, dans certaines expériences de fiabilité, le nombre d'articles tombés en panne ne peut pasêtre préfixé. Dans de telles situations, un système de suppression aléatoire avec un système de censure peut offrir un bon résultat. Ici, un système de suppression aléatoire avec un plan de censure progressif est supposé pour l'inférence statistique, lorsque le nombre de pannes ne peutêtre préfixé. L'analyse de la présente discussion aété effectuéeà l'aide d'un ensemble de données réelles issues de la distribution Burr Type-XII.

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Section: Progressive Censoring With Random Schemementioning

confidence: 99%

Progressive Censored Burr Type-XII Distribution Under Random Removal Scheme: Some Inferences

Prakash¹

2017

JAS

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“…Feroze and El-Batal (2013) derived the maximum likelihood estimators of the Kumaraswamy distribution under progressive type-II censored data with random removals. Azimi et al (2014) discussed statistical inference procedures of the Pareto distribution using progressive type-II censoring data with binomial removals. Chacko and Mohan (2018) discussed statistical inference for Gompertz distribution under progressive type-II censoring with binomial removals.…”

Section: Introductionmentioning

confidence: 99%

Statistical inference for Kumaraswamy-exponential distribution based on progressive Type-II censored data with binomial removals

Mohan

Chacko

2020

JSR

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In this paper, estimation of parameters of Kumaraswamy-exponential distribution with shape parameters α and β is considered based on a progressively type-II censored sample with binomial removals. Together with the unknown parameters, the removal probability p is also estimated. Bayes estimators are obtained using different loss functions such as squared error, LINEX loss function and entropy loss function. All Bayesian estimates are compared with the corresponding maximum likelihood estimates numerically in terms of their bias and mean square error values and found that Bayes estimators perform better than MLE’s for β and p and MLEs perform better than Bayes estimators for α. A real data set is also used for illustration.

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“…Both known and unknown cases of scale parameter have considered in the estimation of bound length. In (2014), Azimi et al (2014) obtained some Bayes estimator of life parameters for the generalized Pareto distribution under Progressive censoring.…”

Section: Introductionmentioning

confidence: 99%

Empirical Estimation Based On Progressive First Failure Censored Generalized Pareto Data

Prakash¹

2018

Jas

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Abstract. The Progressive First-Failure (PFF) censoring scheme is considered in the present article for the Empirical Bayes estimation. The approximate confidence intervals and the Bayes estimation for the unknown parameters under the empirical Bayesian technique are obtained for the Generalized Pareto distribution. The improved approximate confidence intervals are discussed also. A simulation technique is applied here for illustrating the methods based on different censoring plans, those are the special cases of PFF censoring scheme.Résumé. Le schéma de censure du premieréchec progressif (PFF) est considéré dans le présent article pour l'estimation empirique de Bayes. Les intervalles de confiance approximatifs et l'estimation de Bayes pour les paramètres inconnus sous la méthode bayésienne empirique sont obtenus pour la distribution de Pareto généralisée. Les intervalles de confiance approximatifs améliorés sontégalement discutés. Une technique de simulation est appliquée ici pour illustrer les méthodes basées sur différents plans de censure, qui sont les cas particuliers du schéma de censure PFF.

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Statistical inference for generalized Pareto distribution based on progressive Type-II censored data with random removals

Cited by 6 publications

References 12 publications

Progressive Censored Burr Type-XII Distribution Under Random Removal Scheme: Some Inferences

Progressive Censored Burr Type-XII Distribution Under Random Removal Scheme: Some Inferences

Statistical inference for Kumaraswamy-exponential distribution based on progressive Type-II censored data with binomial removals

Empirical Estimation Based On Progressive First Failure Censored Generalized Pareto Data

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