Bayesian Estimation of the Exponential Parameter under a Multiply Type-II Censoring Scheme

Singh, Umesh; Kumar, Anil

doi:10.17713/ajs.v36i3.334

Cited by 10 publications

(10 citation statements)

References 7 publications

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“…Kang and Park (2005) discussed the estimation for exponentiated exponential distribution based on multiply Type-II censoring scheme. Singh and Kumar (2007) provided the Bayes estimators of the exponential parameter under the multiply Type-II censoring scheme. Ganguly et al (2012) studied the exact inference for the two-parameter exponential distribution under the Type-II hybrid censoring scheme.…”

Section: Introductionmentioning

confidence: 99%

Bayesian estimation for the exponential distribution based on generalized multiply Type-II hybrid censoring

Jeon

Kang

2020

CSAM

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The multiply Type-II hybrid censoring scheme is disadvantaged by an experiment time that is too long. To overcome this limitation, we propose a generalized multiply Type-II hybrid censoring scheme. Some estimators of the scale parameter of the exponential distribution are derived under a generalized multiply Type-II hybrid censoring scheme. First, the maximum likelihood estimator of the scale parameter of the exponential distribution is obtained under the proposed censoring scheme. Second, we obtain the Bayes estimators under different loss functions with a noninformative prior and an informative prior. We approximate the Bayes estimators by Lindleys approximation and the Tierney-Kadane method since the posterior distributions obtained by the two priors are complicated. In addition, the Bayes estimators are obtained by using the Markov Chain Monte Carlo samples. Finally, all proposed estimators are compared in the sense of the mean squared error through the Monte Carlo simulation and applied to real data.

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Section: Introductionmentioning

confidence: 99%

Bayesian estimation for the exponential distribution based on generalized multiply Type-II hybrid censoring

Jeon

Kang

2020

CSAM

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“…This incomplete data is called left censored data. Some of the work on left censoring was conducted by [19][20][21][22]. Based on the censored data, two heavy-tailed distributions may provide very similar data fit to a given data set.…”

Section: Introductionmentioning

confidence: 99%

Model Selection Test for the Heavy-Tailed Distributions under Censored Samples with Application in Financial Data

Panahi

2016

IJFS

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Numerous heavy-tailed distributions are used for modeling financial data and in problems related to the modeling of economics processes. These distributions have higher peaks and heavier tails than normal distributions. Moreover, in some situations, we cannot observe complete information about the data. Employing the efficient estimation method and then choosing the best model in this situation are very important. Thus, the purpose of this article is to propose a new interval for comparing the two heavy-tailed candidate models and examine its suitability in the financial data under complete and censored samples. This interval is equivalent to encapsulating the results of many hypotheses tests. A maximum likelihood estimator (MLE) is used for evaluating the parameters of the proposed heavy-tailed distribution. A real dataset representing the top 30 companies of the Tehran Stock Exchange indices is used to illustrate the derived results.

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“…Dey and Kuo (1991) obtained a new class of empirical Bayes estimator for exponential distribution parameter from Type II censored data. Singh and Kumar (2007) considered Bayesian estimation of the exponential parameter under a multiply Type II Censoring scheme. Kundu and Raqab (2012) addressed Bayesian inference for Weibull distribution under Type II censoring scheme.…”

Section: Introductionmentioning

confidence: 99%

Estimating the Parameter of Exponential Distribution under Type II Censoring From Fuzzy Data

Makhdoom¹,

Nasiri²,

Pak³

2016

J. Mod. App. Stat. Meth.

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The problem of estimating the parameter of Exponential distribution on the basis of type II censoring scheme is considered when the available data are in the form of fuzzy numbers. The Bayes estimate of the unknown parameter is obtained by using the approximation forms of Lindley (1980) and Tierney and Kadane (1986) under the assumption of gamma prior. The highest posterior density (HPD) estimate of the parameter of interest is found. A Monte Carlo simulation is used to compare the performances of the different methods. A real data set is investigated to illustrate the applicability of the proposed methods.

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Bayesian Estimation of the Exponential Parameter under a Multiply Type-II Censoring Scheme

Cited by 10 publications

References 7 publications

Bayesian estimation for the exponential distribution based on generalized multiply Type-II hybrid censoring

Bayesian estimation for the exponential distribution based on generalized multiply Type-II hybrid censoring

Model Selection Test for the Heavy-Tailed Distributions under Censored Samples with Application in Financial Data

Estimating the Parameter of Exponential Distribution under Type II Censoring From Fuzzy Data

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