BOOTSTRAPPING CERTAIN MEASURES OF LOCATION
Keywords:
Relative Efficiency, Winsorized Mean, Median, Trimmed Mean, Bootstrap Standard ErrorAbstract
The focus of this paper is on consistent estimates of the standard error of certain measures of location. The bootstrap approach was adopted to compute the standard error for assessing the relative efficiencies of some measures of location. The R statistical package was employed to obtain data from some distributions and real data for the analysis. Employing the re-sampling procedure inherent in bootstrapping, it was established analytically that bootstrap standard errors are smaller for the median estimator than their counterpart. The median was found to be the most robust since it produces the least bootstrap standard error and relative efficiency of less than one when compared to the other estimators under study. However, the mean was the most efficient compared to the other measures of the location under study when the distribution is normally distributed.
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