2015Mathematical theory and modelingRequires access

Minimax Estimation of the Scale Parameter of Laplace Distribution under Squared-Log Error Loss Function

Aseel Abdul Razzak Rasheed

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Abstract

In this paper, we obtained Minimax estimator of the scale parameter �� for the Laplace distribution under the Squared log error loss function by applying the theorem of Lehmann [1950], and compared it with Minimax estimator under Quadratic loss function in addition of Maximum Likelihood Estimator according to Monte-Carlo simulation study. The performance of these estimators is compared depending on the mean squared errors (MSE’s).

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What this paper is about

In this paper, we obtained Minimax estimator of the scale parameter �� for the Laplace distribution under the Squared log error loss function by applying the theorem of Lehmann [1950], and compared it with Minimax estimator under Quadratic loss function in addition of Maximum Likelihood Estimator according to Monte-Carlo simulation study. The performance of these estimators is compared depending on the mean squared errors (MSE’s).

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Available abstract

In this paper, we obtained Minimax estimator of the scale parameter �� for the Laplace distribution under the Squared log error loss function by applying the theorem of Lehmann [1950], and compared it with Minimax estimator under Quadratic loss function in addition of Maximum Likelihood Estimator according to Monte-Carlo simulation study. The performance of these estimators is compared depending on the mean squared errors (MSE’s).

Key concepts: Mathematics, Mean squared error, Estimator, Minimax, Minimax estimator, Statistics, Efficient estimator, Applied mathematics

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