The problem of bootstrapping the estimator's variance under a probability proportional to size design is examined. Focusing on the Horvitz-Thompson estimator, three pi PS-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.

Barbiero, A., Mecatti, F. (2010). Bootstrap algorithms for variance estimation in complex survey sampling. In P. Mantovan, P. Secchi (a cura di), Complex data modeling and computationally intensive statistical methods (pp. 57-69). Springer [10.1007/978-88-470-1386-5_5].

Bootstrap algorithms for variance estimation in complex survey sampling

MECATTI, FULVIA
2010

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 pi PS-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.
Capitolo o saggio
Horvitz-Thompson estimator; probability-proportional-to-size sampling; simulation
English
Complex data modeling and computationally intensive statistical methods
Mantovan, P; Secchi, P
2010
978-88-470-1385-8
Springer
57
69
Barbiero, A., Mecatti, F. (2010). Bootstrap algorithms for variance estimation in complex survey sampling. In P. Mantovan, P. Secchi (a cura di), Complex data modeling and computationally intensive statistical methods (pp. 57-69). Springer [10.1007/978-88-470-1386-5_5].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/20382
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