Comparing three bootstrap methods for survey data
- 1 June 1992
- journal article
- Published by Wiley in The Canadian Journal of Statistics / La Revue Canadienne de Statistique
- Vol. 20 (2) , 135-154
- https://doi.org/10.2307/3315464
Abstract
Various bootstrap methods for variance estimation and confidence intervals in complex survey data, where sampling is done without replacement, have been proposed in the literature. The oldest, and perhaps the most intuitively appealing, is the without‐replacement bootstrap (BWO) method proposed by Gross (1980). Unfortunately, the BWO method is only applicable to very simple sampling situations. We first introduce extensions of the BWO method to more complex sampling designs. The performance of the BWO and two other bootstrap methods, the rescaling bootstrap (Rao and Wu 1988) and the mirror‐match bootstrap (Sitter 1992), are then compared through a simulation study. Together these three methods encompass the various bootstrap proposals.Keywords
This publication has 13 references indexed in Scilit:
- A Resampling Procedure for Complex Survey DataJournal of the American Statistical Association, 1992
- Quantile Estimation with a Complex Survey DesignThe Annals of Statistics, 1991
- Balanced repeated replications based on mixed orthogonal arraysBiometrika, 1991
- Resampling Inference with Complex Survey DataJournal of the American Statistical Association, 1988
- Mixed orthogonal arrays for variance estimation with unequal numbers of primary selections per stratumBiometrika, 1987
- Edgeworth Corrected Pivotal Statistics and the BootstrapThe Annals of Statistics, 1985
- Asymptotic Normality and the Bootstrap in Stratified SamplingThe Annals of Statistics, 1984
- An Evaluation of Model-Dependent and Probability-Sampling Inferences in Sample SurveysJournal of the American Statistical Association, 1983
- Constructing Orthogonal Replications for Variance EstimationJournal of the American Statistical Association, 1975
- Confidence Intervals for Medians and Other Position MeasuresJournal of the American Statistical Association, 1952