Abstract
The problem of bootstrapping the estimator’s variance under a probability proportional to size design is examined. Focusing on the Horvitz-Thompson estimator, three πPS-bootstrap algorithms are introduced with the purpose of both simplifying available procedures and of improving efficiency. Results from a simulation study using both natural and artificial data are presented in order to empirically investigate the properties of the provided bootstrap variance estimators.
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Barbiero, A., Mecatti, F. (2010). Bootstrap algorithms for variance estimation in πPS sampling. In: Mantovan, P., Secchi, P. (eds) Complex Data Modeling and Computationally Intensive Statistical Methods. Contributions to Statistics. Springer, Milano. https://doi.org/10.1007/978-88-470-1386-5_5
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DOI: https://doi.org/10.1007/978-88-470-1386-5_5
Publisher Name: Springer, Milano
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