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A Monte Carlo Study of Robustness of Pretest and Shrinkage Estimators in Pooling Coefficients of Variation
Author(s) -
Ahmed S.E.,
Bhoj D.S.,
Ahsanullah M.
Publication year - 1998
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/(sici)1521-4036(199810)40:6<737::aid-bimj737>3.0.co;2-g
Subject(s) - estimator , shrinkage , shrinkage estimator , monte carlo method , mathematics , statistics , pooling , robustness (evolution) , a priori and a posteriori , mean squared error , coefficient of variation , bias of an estimator , computer science , minimum variance unbiased estimator , artificial intelligence , biochemistry , chemistry , philosophy , epistemology , gene
Pooling data, when justified, is advantageous for estimating the true parameter. In this paper the problem of estimating the coefficient of variation is considered when it is a priori suspected that two coefficients of variation are the same. Various estimators based on pretest and shrinkage rules are considered. A comparison through the Simulated Mean Squared Error (SMSE) criterion is carried out among various proposed estimators of the target coefficient of variation. The relative simulated efficiencies of the restricted, shrinkage restricted and shrinkage pretest estimators are studied. It is found that the proposed estimators are quite robust when the sample sizes are not too large. The result of Monte Carlo study indicates that the proposed shrinkage pretest estimator is efficient than the usual estimator in a wider range.