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A new resampling method for sampling designs without replacement: the doubled half bootstrap
Abstract A new and very fast method of bootstrap for sampling without replacement from a finite population is proposed. This method can be used to estimate the variance in sampling with unequal inclusion probabilities and does not require artificial populations or utilization of bootstrap weights. The bootstrap samples are directly selected from the original sample. The bootstrap procedure contains two steps: in the first step, units are selected once with Poisson sampling using the same inclusion probabilities as the original design. In the second step, amongst the non-selected units, half of the units are randomly selected twice. This procedure enables us to efficiently estimate the variance. A set of simulations show the advantages of this new resampling method.
   
Keywords Poisson sampling, Simple random sampling, Unequal probability sampling, Variance estimation
   
Citation Antal, E., & Tillé, Y. (2014). A new resampling method for sampling designs without replacement: the doubled half bootstrap. Computational Statistics, 29(5), 1345-1363.
   
Type Journal article (English)
Date of appearance 10-2014
Journal Computational Statistics
Volume 29
Issue 5
Pages 1345-1363
URL http://link.springer.com/article/10.1007/s00180-014-0495-0
Related project Convention Université de Neuchâtel/Office fédéral de la s...