A bootstrap algorithm is proposed for the case where sampling data are from a finite population without replacement and with probability proportional to size. It is shown that the algorithm is a natural modification of the Efron's original bootstrap as it conforms to the fundamental bootstrap principles for iid sampling data from continuous distributions. Furthermore it works as a generalization of the method by Chao and lo for simple random samples
Mecatti, F. (2000). Bootstrapping Unequal Probability Samples. STATISTICA APPLICATA, 12(1), 67-77 [http://sa-ijas.stat.unipd.it/sites/sa-ijas.stat.unipd.it/files/67-77.pdf].
Bootstrapping Unequal Probability Samples
MECATTI, FULVIA
2000
Abstract
A bootstrap algorithm is proposed for the case where sampling data are from a finite population without replacement and with probability proportional to size. It is shown that the algorithm is a natural modification of the Efron's original bootstrap as it conforms to the fundamental bootstrap principles for iid sampling data from continuous distributions. Furthermore it works as a generalization of the method by Chao and lo for simple random samples
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