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A comparison of estimators for the mean in the inverse gaussian distribution with a known coefficient of variation
Abstract:

In this paper, we discuss an estimation problem of the mean in the inverse Gaussian distribution with a known coefficient of variation. Two types of linear estimators for the mean, the linear minimum variance unbiased estimator and the linear minimum mean squared error estimator, are constructed by using the squared error loss function and their properties are examined. It is observed that, for small samples the performance of the proposed estimators is better than that of the maximum likelihood estimator, when the coefficient of variation is large.
Keywords:Inverse Gaussian Distribution  Coefficient Of Variation  Linear Minimum Variance Unbiased Estimator  Linear Minimum Mean Squared Error Estimator  Relative Efficiency
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