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Bayesian Shrinkage Estimator for the Scale Parameter of Exponential Distribution Based on Censored Data
Azadeh Kiapour
Abstract:   (2641 Views)

Usually, we estimate the unknown parameter by observing a random sample and using the usual methods of estimation such as maximum likelihood method. In some situations, we have information about the real parameter in the form of a guess. In these cases, one may shrink the maximum likelihood or other estimators towards a guess value and construct a shrinkage estimator. In this paper, we study the behavior of a Bayes shrinkage estimator for the scale parameter of exponential distribution based on censored samples under an asymmetric and scale invariant loss function. To do this, we propose a Bayes shrinkage estimator and compute the relative efficiency between this estimator and the best linear estimator within a subclass with respect to sample size, hyperparameters of the prior distribution and the vicinity of the guess and real parameter. Also, the obtained results are extended to Weibull and Rayleigh lifetime distributions.

In this paper‎, ‎we study the performance of the Bayesian shrinkage estimator for the scale parameter of‎ ‎exponential distribution based on censored samples under an asymmetric scale invariant loss function‎. ‎To do this,‎ ‎we propose the Bayesian shrinkage estimator and compute the relative efficiency of this estimator with respect to the‎ ‎improved estimator.

Keywords: Bayesian shrinkage estimator‎, ‎Exponential distribution‎, ‎Censored data
Full-Text [PDF 4004 kb]   (516 Downloads)    
Type of Study: Research | Subject: Statistical Inference
Received: 2015/11/28 | Accepted: 2017/02/9 | Published: 2017/02/27
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Kiapour A. Bayesian Shrinkage Estimator for the Scale Parameter of Exponential Distribution Based on Censored Data . J. of Stat. Sci.. 2017; 11 (1)
URL: http://jss.irstat.ir/article-1-425-en.html
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مجله علوم آماری – نشریه علمی پژوهشی انجمن آمار ایران Journal of Statistical Sciences
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