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Jackknife repeated replication (JRR) is a method to estimate the sampling variability of a statistic that takes into account the properties of the sample design. It provides unbiased estimates of the sampling error arising from complex sample selection procedures; reflects the component of sampling error introduced by the use of weighting factors that are dependent on the sample data obtained; and can be readily adapted to the estimation of sampling errors for parameters estimated using statistical modeling procedures. The general idea behind the Jackknife is to split a single sample into multiple subsamples and use the fluctuation among the subsamples to obtain an estimate of the overall sampling variability. The first step in this procedure is to divide the full sample into random groups. In turn, each group is removed from the full sample in order to create a subsample. The JRR procedure derives estimates of the parameter of interest from each of the subsamples, and calculates the variance of the full-sample estimate from the variability between the subsample estimates.
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