Two compound Rayleigh lifetime distributions in analyses the jointly type-II censoring samples: DSGT2018

Algarni, Ali; Almarashi, Abdullah M.; Abd-Elmougod, G. A.; Abo-Eleneen, Z. A.

doi:10.1007/s10910-019-01058-5

Cited by 20 publications

(12 citation statements)

References 14 publications

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“…Barot and Patel [8] developed Bayesian and Posterior risk approach based on progressive type-II censored data from a CRD under the balanced loss functions. Algarni et al [2] used two CR lifetime models to analyze the jointly type-II censoring data. They obtained classical estimators such as Maximum Likelihood for CRD.…”

Section: Introductionmentioning

confidence: 99%

E-Bayesian Estimations and Its E-MSE For Compound Rayleigh Progressive Type-II Censored Data

Shojaee

Piriaei

Babanezhad

2022

Stat., optim. inf. comput.

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Over the past decades, various methods to estimate the unknown parameter, the survival function, and the hazard rate of a statistical distribution have been proposed from the availability of type-II censored data. They are all differing in terms of how the progressive type-II censored data of the underlying distribution are available. In this study, we estimate the parameter, the survival function, and the hazard rate of the compound Rayleigh distribution by using the E-Bayesian estimation when the progressive type-II censored data are available. The resulting estimators are evaluated based on the asymmetric general entropy and the symmetric squared error loss functions. In addition, the E-Bayesian estimators under the different loss functions have been compared through a real data analysis and Monte Carlo simulation studies by calculating the E-MSE of the resulting estimators.

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Section: Introductionmentioning

confidence: 99%

E-Bayesian Estimations and Its E-MSE For Compound Rayleigh Progressive Type-II Censored Data

Shojaee

Piriaei

Babanezhad

2022

Stat., optim. inf. comput.

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“…Different properties of the competing risks model are exposed by Crowder [14], Balakrishnan [15], and Bakoban and Abd-Elmougod [16]. Recently, the properties of a competing risks model under the accelerated life test model are discussed by Ganguly and Kundu [17] and Algarni et al [18]. In a random sample of size n of the life products and predetermined integer munder the competing risks model, the two causes of failure are independent.…”

Section: Introductionmentioning

confidence: 99%

Inferences for Joint Hybrid Progressive Censored Exponential Lifetimes under Competing Risk Model

Almarashi

Algarni

Daghistani

et al. 2021

Mathematical Problems in Engineering

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The aim of this paper is devoted to the problem of comparative life tests under joint censoring samples from an exponential distribution with competing risks model. This problem is considered under the consideration that only two causes of failure are occurring and the units come from two production lines such that the exponential failure time of units is censored under a hybrid progressive Type-I censoring scheme. Maximum likelihood estimation and different Bayes methods of estimation are discussed. The asymptotic confidence intervals as well as the Bayes credible intervals are established. A real data set representing time to failure on two groups of strain male mice receiving radiation is analyzed for illustrative purposes. All theoretical results are assessed and compared through the Monte Carlo study.

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“…For progressive joint sample, refer studies by Rasouli A and Balakrishnan [10,11] and recently by B. N. Al-Matrafi and G. A. Abd-Elmougod [12]. Also, for the accelerate model of Rayleigh distribution, refer studies by Faten A. Momenkhan and Abd-Elmougod [13,14].…”

Section: Introductionmentioning

confidence: 99%

Statistical Analysis of Joint Type‐I Generalized Hybrid Censoring Data from Burr XII Lifetime Distributions

et al. 2021

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The quality of the products coming from different lines of production requires some tests called comparative life tests. For lines having the same facility, the lifetime of the product is distributed by Burr XII, the lifetime distribution, and units are tested under type-I generalized hybrid censoring scheme. The observed censoring data are used under maximum likelihood and the Bayes method to estimate the model parameters. The theoretical results are discussed and assessed through data analysis and Monte Carlo simulation study. Finally, we reported some brief comments obtained from numerical computation.

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Two compound Rayleigh lifetime distributions in analyses the jointly type-II censoring samples: DSGT2018

Cited by 20 publications

References 14 publications

E-Bayesian Estimations and Its E-MSE For Compound Rayleigh Progressive Type-II Censored Data

E-Bayesian Estimations and Its E-MSE For Compound Rayleigh Progressive Type-II Censored Data

Inferences for Joint Hybrid Progressive Censored Exponential Lifetimes under Competing Risk Model

Statistical Analysis of Joint Type‐I Generalized Hybrid Censoring Data from Burr XII Lifetime Distributions

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