On the comparison of the pre-test and shrinkage estimators for the univariate normal mean
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The estimation of the mean of an univariate normal population with unknown variance is considered when uncertain non-sample prior information is available. Alternative estimators are denned to incorporate both the sample as well as the non-sample information in the estimation process. Some of the important statistical properties of the restricted, preliminary test, and shrinkage estimators are investigated. The performances of the estimators are compared based on the criteria of unbiasedness and mean square error in order to search for a ‘best’ estimator. Both analytical and graphical methods are explored. There is no superior estimator that uniformly dominates the others. However, if the non-sample information regarding the value of the mean is close to its true value, the shrinkage estimator over performs the rest of the estimators.
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- On the comparison of the pre-test and shrinkage estimators for the univariate normal mean
Volume 42, Issue 4 , pp 451-473
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- Uncertain non-sample prior information
- maximum likelihood, restricted, preliminary test and shrinkage estimators
- bias, mean square error and relative efficiency
- normal, Student-t, non-central chi-square and F distributions
- incomplete beta ratio
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