Filter results by
Search HelpKeyword(s)
Subject
Author(s)
Results
All (3)
All (3) ((3 results))
- Articles and reports: 12-001-X20070019853Description:
Two-phase sampling is a useful design when the auxiliary variables are unavailable in advance. Variance estimation under this design, however, is complicated particularly when sampling fractions are high. This article addresses a simple bootstrap method for two-phase simple random sampling without replacement at each phase with high sampling fractions. It works for the estimation of distribution functions and quantiles since no rescaling is performed. The method can be extended to stratified two-phase sampling by independently repeating the proposed procedure in different strata. Variance estimation of some conventional estimators, such as the ratio and regression estimators, is studied for illustration. A simulation study is conducted to compare the proposed method with existing variance estimators for estimating distribution functions and quantiles.
Release date: 2007-06-28 - Articles and reports: 12-001-X20060029549Description:
In this article, we propose a Bernoulli-type bootstrap method that can easily handle multi-stage stratified designs where sampling fractions are large, provided simple random sampling without replacement is used at each stage. The method provides a set of replicate weights which yield consistent variance estimates for both smooth and non-smooth estimators. The method's strength is in its simplicity. It can easily be extended to any number of stages without much complication. The main idea is to either keep or replace a sampling unit at each stage with preassigned probabilities, to construct the bootstrap sample. A limited simulation study is presented to evaluate performance and, as an illustration, we apply the method to the 1997 Japanese National Survey of Prices.
Release date: 2006-12-21 - 3. A repeated half-sample bootstrap and balanced repeated replications for randomly imputed data ArchivedArticles and reports: 12-001-X20010026095Description:
In this paper, we discuss the application of the bootstrap with a re-imputation step to capture the imputation variance (Shao and Sitter 1996) in stratified multistage sampling. We propose a modified bootstrap that does not require rescaling so that Shao and Sitter's procedure can be applied to the case where random imputation is applied and the first-stage stratum sample sizes are very small. This provides a unified method that works irrespective of the imputation method (random or nonrandom), the stratum size (small or large), the type of estimator (smooth or nonsmooth), or the type of problem (variance estimation or sampling distribution estimation). In addition, we discuss the proper Monte Carlo approximation to the bootstrap variance, when using re-imputation together with resampling methods. In this setting, more care is needed than is typical. Similar results are obtained for the method of balanced repeated replications, which is often used in surveys and can be viewed as an analytic approximation to the bootstrap. Finally, some simulation results are presented to study finite sample properties and various variance estimators for imputed data.
Release date: 2002-02-28
Stats in brief (0)
Stats in brief (0) (0 results)
No content available at this time.
Articles and reports (3)
Articles and reports (3) ((3 results))
- Articles and reports: 12-001-X20070019853Description:
Two-phase sampling is a useful design when the auxiliary variables are unavailable in advance. Variance estimation under this design, however, is complicated particularly when sampling fractions are high. This article addresses a simple bootstrap method for two-phase simple random sampling without replacement at each phase with high sampling fractions. It works for the estimation of distribution functions and quantiles since no rescaling is performed. The method can be extended to stratified two-phase sampling by independently repeating the proposed procedure in different strata. Variance estimation of some conventional estimators, such as the ratio and regression estimators, is studied for illustration. A simulation study is conducted to compare the proposed method with existing variance estimators for estimating distribution functions and quantiles.
Release date: 2007-06-28 - Articles and reports: 12-001-X20060029549Description:
In this article, we propose a Bernoulli-type bootstrap method that can easily handle multi-stage stratified designs where sampling fractions are large, provided simple random sampling without replacement is used at each stage. The method provides a set of replicate weights which yield consistent variance estimates for both smooth and non-smooth estimators. The method's strength is in its simplicity. It can easily be extended to any number of stages without much complication. The main idea is to either keep or replace a sampling unit at each stage with preassigned probabilities, to construct the bootstrap sample. A limited simulation study is presented to evaluate performance and, as an illustration, we apply the method to the 1997 Japanese National Survey of Prices.
Release date: 2006-12-21 - 3. A repeated half-sample bootstrap and balanced repeated replications for randomly imputed data ArchivedArticles and reports: 12-001-X20010026095Description:
In this paper, we discuss the application of the bootstrap with a re-imputation step to capture the imputation variance (Shao and Sitter 1996) in stratified multistage sampling. We propose a modified bootstrap that does not require rescaling so that Shao and Sitter's procedure can be applied to the case where random imputation is applied and the first-stage stratum sample sizes are very small. This provides a unified method that works irrespective of the imputation method (random or nonrandom), the stratum size (small or large), the type of estimator (smooth or nonsmooth), or the type of problem (variance estimation or sampling distribution estimation). In addition, we discuss the proper Monte Carlo approximation to the bootstrap variance, when using re-imputation together with resampling methods. In this setting, more care is needed than is typical. Similar results are obtained for the method of balanced repeated replications, which is often used in surveys and can be viewed as an analytic approximation to the bootstrap. Finally, some simulation results are presented to study finite sample properties and various variance estimators for imputed data.
Release date: 2002-02-28
Journals and periodicals (0)
Journals and periodicals (0) (0 results)
No content available at this time.
- Date modified: