摘要：The main objective of this paper is to find the minimax estimator of the scale parameter of
Laplace distribution under MLINEX loss function by applying the theorem of Lehmann
(1950). The estimator is then compared with classical estimator like moment estimator with
respect to mean square errors (MSEs) through R- Code simulation. The result has shown
that the minimax estimator under MLINEX loss function is better than moment estimator
for all sample sizes. Finally, mean square errors of different estimators corresponding to
sample size are presented graphically.
其他摘要：The main objective of this paper is to find the minimax estimator of the scale parameter of Laplace distribution under MLINEX loss function by applying the theorem of Lehmann (1950). The estimator is then compared with classical estimator like moment estimator with respect to mean square errors (MSEs) through R- Code simulation. The result has shown that the minimax estimator under MLINEX loss function is better than moment estimator for all sample sizes. Finally, mean square errors of different estimators corresponding to sample size are presented graphically.
关键词：Minimax estimator; Moment estimator; Jeffrey prior; Bayes estimator; MLINEX
Loss Function
