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
Bootstrap algorithms for variance estimation in πPS sampling / A. Barbiero, F. Mecatti - In: Complex data modeling and computationally intensive statistical methods / [a cura di] P. Mantovan, P. Secchi. - Milano : Springer Italia, 2010. - ISBN 978-88-470-1385-8.
Bootstrap algorithms for variance estimation in πPS sampling
A. BarbieroPrimo
;
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 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
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
