Prediction for Pareto distribution based on progressively Type-II censored samples

Raqab, Mohammad Z.; Asgharzadeh, A.; Valiollahi, Reza

doi:10.1016/j.csda.2010.02.005

Cited by 74 publications

(29 citation statements)

References 9 publications

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“…It is obvious that L 1 in (12) does not involve p. Thus, the maximum likelihood estimate (MLE) of θ can be derived by maximizing (12) directly. On the other hand, L 2 in (13) does not depend on the parameter θ, then the MLE of p can be obtained directly by maximizing (13).…”

Section: Point Estimationmentioning

confidence: 99%

“…Inference, sampling design and generalization based on progressively censored samples were studied by many authors, see among others, Balasooriya et al [3], Ng et al [4], Balakrishnan et al [5], Fernandez [6], Soliman [7], Asgharzadeh [8], Ku and Kaya [9], Wu et al [10], Banerjee and Kundu [11] and Raqab et al [12]. However, a little work is introduced in the Bayesian context.…”

Section: Introductionmentioning

confidence: 99%

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Inferences using type-II progressively censored data with binomial removals

et al. 2015

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This paper considers the estimation problem for Burr type-X model, when the lifetimes are collected under type-II progressive censoring with random removals, where the number of units removed at each failure time follows a binomial distribution. The methods of maximum likelihood as well as the Bayes procedure to derive both point and interval estimates of the parameters are used. The expected test time to complete the censoring test is computed and analyzed for different censoring schemes. The effect of the binomial distribution parameter p on the expected test time under progressive censoring and the relative expected test time over the complete sample are investigated. Monte Carlo simulations are performed to compare and evaluate the performance of different methods. Furthermore, an example with a real data set is presented for illustrative purposes.

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Section: Point Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Inferences using type-II progressively censored data with binomial removals

et al. 2015

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“…Last one decade Progressive censoring scheme has received considerable interest among the researchers. A little few of them Balakrishnan & Sandhu (1995), Balakrishnan & Aggarwala (2000), Kundu (2008), Lee et al (2009), Raqab et al (2010, Fu et al (2012), Al-Zahrani & Al-Sobhi (2013), Prakash (2015) and Prakash (2016).…”

Section: First-failure Progressive Censoring Schemementioning

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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“…This experiment terminates at the time when the m th failure is observed and the remaining r m = n -r 1 -... -r m-1 -m surviving units are all removed. The statistical inference on the parameters of failure time distributions under progressive Type II censoring has been studied by several authors, such as Cohen (1963), Mann (1971), Viveros and Balakrishnan (1974), Balakrishnan and Aggarwala (2000), Ng et al (2002), Chan and Balakrishnan (2004), Soliman (2008) and Raqab et al (2010) (and the references therein). Note that, in this scheme, r 1 , r 2 , ..., r m are all pre-fixed.…”

Section: Introductionmentioning

confidence: 99%

Parameter Estimations Based On Kumaraswamy Progressive Type II Censored Data with Random Removals

Feroze

Elbatal

2013

J. Mod. App. Stat. Meth.

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The estimation of two parameters of the Kumaraswamy distribution is considered under Type II progressive censoring with random removals, where the number of units removed at each failure time has a binomial distribution. The MLE was used to obtain the estimators of the unknown parameters, and the asymptotic variance -covariance matrix was also obtained. The formula to compute the expected test time was derived. A numerical study was carried out for different combinations of model parameters. Different censoring schemes were used for the estimation, and performance of these schemes was compared.

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Prediction for Pareto distribution based on progressively Type-II censored samples

Cited by 74 publications

References 9 publications

Inferences using type-II progressively censored data with binomial removals

Inferences using type-II progressively censored data with binomial removals

Empirical Estimation Based On Progressive First Failure Censored Generalized Pareto Data

Parameter Estimations Based On Kumaraswamy Progressive Type II Censored Data with Random Removals

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