Estimation of scale parameter under entropy loss function

Parsian, Ahmad; Nematollahi, Nader

doi:10.1016/0378-3758(95)00026-7

Cited by 52 publications

(16 citation statements)

References 17 publications

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“…The idea of Corollary 1 was introduced in Parsian and Nematollahi (1996) via some examples, defining pivotal quantities for well-known distributions. A list of the distributions satisfying Theorem 1 is given in Table 1.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Confidence Intervals for the Scale Parameter of Exponential Family of Distributions

Hoshyarmanesh¹,

Karami²,

Mohammadpour³

2016

The American Statistician

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This article presents a unified approach for computing non-equal tail optimal confidence intervals for the scale parameter of the exponential family of distributions. We prove that there exists a pivotal quantity, as a function of a complete sufficient statistic, with a chi-square distribution. Using the similarity between equations of shortest, unbiased and highest density confidence intervals, all equations are reduced into a system of two equations that can be solved via a straightforward algorithm.KEY WORDS: Equal tail, shortest and unbiased confidence intervals; Highest posterior density credible interval; Pivotal quantity; Scale-exponential family.Hossein Hoshyarmanesh and Amirhossein Karami are graduate students, and Adel Mohammadpour is an associate professor (

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Section: Accepted Manuscriptmentioning

confidence: 99%

Confidence Intervals for the Scale Parameter of Exponential Family of Distributions

Hoshyarmanesh¹,

Karami²,

Mohammadpour³

2016

The American Statistician

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“…However, the range of hyperparameters given by experts can be reasonable and practical. Based on the range of hyperparameters , : , 1 0 k 0 proposed in [8], prior density function can be selected as…”

Section: E-bayesian Estimation Of System Reliabilitymentioning

confidence: 99%

Reliability evaluation for m-consecutive-k-out-of-n: F system with Burr XII components

Shi

Sun

2011

2011 International Conference on Multimedia Technology

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Based on Type-II doubly censored samples, we study the reliability evaluation of m-consecutive -k-out-of-n: F system with Burr XII components. Classical statistical method and Bayes approach have been used to derive the MLE, Bayesian estimator and E-Bayesian estimator for unknown parameter and system reliability. The statistical performances are compared through the accuracy of MSE based on Monte-Carlo simulation. According to these comparisons, it is concluded that the E-Bayesian estimations are preferable.

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“…See Parsian and Nematollahi (1996) and the references therein for more details on a review of literatures. Rios Inusa et al (1995) discussed PRGM estimation under squared error loss.…”

Section: Introductionmentioning

confidence: 99%

Posterior Regret Γ-Minimax Estimation and Prediction with Applications onk-Records Data Under Entropy Loss Function

Jozani

Parsian

2008

Communications in Statistics - Theory and Methods

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Robust Bayesian analysis is connected with the effect of changing a prior within a classinstead of being specified exactly. The multiplicity of prior leads to a collection or a range of Bayes actions. It is interesting not only to investigate the range of estimators but also to recommend the optimal procedures. In this article, we deal with posterior regret -minimax PRGM estimation and prediction of an unknown parameter and a value of a random variable Y under entropy loss function. Applications for k-records such as estimation and prediction problems are discussed.

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Estimation of scale parameter under entropy loss function

Cited by 52 publications

References 17 publications

Confidence Intervals for the Scale Parameter of Exponential Family of Distributions

Confidence Intervals for the Scale Parameter of Exponential Family of Distributions

Reliability evaluation for m-consecutive-k-out-of-n: F system with Burr XII components

Posterior Regret Γ-Minimax Estimation and Prediction with Applications onk-Records Data Under Entropy Loss Function

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