2016SSRN Electronic JournalOpen access

Semi-Minimax Estimators of Maxwell Distribution under New Loss Function

Huda Abdullah, Zainab Khalifa

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Abstract

In this paper, we obtained some Bayesian estimators of the parameter of Maxwell distribution under New loss function. In order to get a better understanding of our Bayesian analysis, we consider the conjugate prior density, based on a Monte Carlo simulation study. The performance of those estimators have been compared using the mean square error's (MSE's) as the comparison criteria. The result showed that, the Bayesian estimators with Inverted Gamma prior are the best for estimating the scale parameter of Maxwell distribution.

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What this paper is about

In this paper, we obtained some Bayesian estimators of the parameter of Maxwell distribution under New loss function. In order to get a better understanding of our Bayesian analysis, we consider the conjugate prior density, based on a Monte Carlo simulation study. The performance of those estimators have been compared using the mean square error's (MSE's) as the comparison criteria. The result showed that, the Bayesian estimators with Inverted Gamma prior are the best for estimating the scale parameter of Maxwell distribution.

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Available abstract

In this paper, we obtained some Bayesian estimators of the parameter of Maxwell distribution under New loss function. In order to get a better understanding of our Bayesian analysis, we consider the conjugate prior density, based on a Monte Carlo simulation study. The performance of those estimators have been compared using the mean square error's (MSE's) as the comparison criteria. The result showed that, the Bayesian estimators with Inverted Gamma prior are the best for estimating the scale parameter of Maxwell distribution.

Key concepts: Estimator, Minimax, Mathematics, Conjugate prior, Applied mathematics, Mean squared error, Bayesian probability, Prior probability

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