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
Articolo in rivista - Articolo scientifico
finite population bootstrap, bootstrap principles, complex sampling design
English
2000
12
1
67
77
reserved
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].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/33215
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