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- 21. Efficient bootstrap for business surveys ArchivedArticles and reports: 12-001-X200700210494Description:
The Australian Bureau of Statistics has recently developed a generalized estimation system for processing its large scale annual and sub-annual business surveys. Designs for these surveys have a large number of strata, use Simple Random Sampling within Strata, have non-negligible sampling fractions, are overlapping in consecutive periods, and are subject to frame changes. A significant challenge was to choose a variance estimation method that would best meet the following requirements: valid for a wide range of estimators (e.g., ratio and generalized regression), requires limited computation time, can be easily adapted to different designs and estimators, and has good theoretical properties measured in terms of bias and variance. This paper describes the Without Replacement Scaled Bootstrap (WOSB) that was implemented at the ABS and shows that it is appreciably more efficient than the Rao and Wu (1988)'s With Replacement Scaled Bootstrap (WSB). The main advantages of the Bootstrap over alternative replicate variance estimators are its efficiency (i.e., accuracy per unit of storage space) and the relative simplicity with which it can be specified in a system. This paper describes the WOSB variance estimator for point-in-time and movement estimates that can be expressed as a function of finite population means. Simulation results obtained as part of the evaluation process show that the WOSB was more efficient than the WSB, especially when the stratum sample sizes are sometimes as small as 5.
Release date: 2008-01-03 - 22. Mean - Adjusted bootstrap for two - Phase sampling ArchivedArticles 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 - 24. Link-tracing sampling with an initial sample of sites sequentially selected: Estimation of the population size ArchivedArticles and reports: 11-522-X20040018750Description:
This paper modifies the link-tracing sampling with a sequential sample of sites and proposes a maximum likelihood estimator or another one derived under the Bayesian approach. It proposes that confidence intervals be constructed by Bootstrap methods.
Release date: 2005-10-27 - Articles and reports: 12-002-X20050018030Description:
People often wish to use survey micro-data to study whether the rate of occurrence of a particular condition in a subpopulation is the same as the rate of occurrence in the full population. This paper describes some alternatives for making inferences about such a rate difference and shows whether and how these alternatives may be implemented in three different survey software packages. The software packages illustrated - SUDAAN, WesVar and Bootvar - all can make use of bootstrap weights provided by the analyst to carry out variance estimation.
Release date: 2005-06-23 - Articles and reports: 12-002-X20050018031Description:
This article presents revisions to a Stata "bswreg" ado file that calculates variance estimates using bootstrap weights. This revision adds new output and analytic features. The main feature added to the program enables researchers to apply mean bootstrap weights while accounting for the number of weights used to generate the average bootstrap weight. The Workplace and Employee Survey dataset will be used to illustrate the usefulness of this program. This revised version of the "bswreg" command is still an easy to use flexible tool, which is compatible with a wide variety of regression analytical techniques and datasets. The bswreg command and design-based bootstrap weights should only be used for inference when it is theoretically valid.
Release date: 2005-06-23 - Articles and reports: 11-522-X20030017720Description:
This paper examines a jackknife method proposed in Rao (2003) for estimating mean squared errors (MSEs) when generalized linear models or other non-linear models are used for the response of interest. It demonstrate the method's performance in a simulation study.
Release date: 2005-01-26 - 28. Using bootstrap weights with Wes Var and SUDAAN ArchivedArticles and reports: 12-002-X20040027032Description:
This article examines why many Statistics Canada surveys supply bootstrap weights with their microdata for the purpose of design-based variance estimation. Bootstrap weights are not supported by commercially available software such as SUDAAN and WesVar, but there are ways to use these applications to produce boostrap variance estimates.
The paper concludes with a brief discussion of other design-based approaches to variance estimation as well as software, programs and procedures where these methods have been employed.
Release date: 2004-10-05 - Articles and reports: 11-522-X20020016713Description:
This paper explores the relationship between low income and prevalence of asthma. The genetic and environmental determinants are incompletely understood. It has been observed in a previous study that Canadians with low incomes are at increased risk of asthma. Based on data from 17,605 subjects 12 years of age or older who participated in the first cycle of the National Population Health Survey (NPHS) from 1994 to 1995, males and females with low incomes had 1.44- and 1.33-fold increases, respectively, in the prevalence of asthma compared with their counterparts with high incomes. However, there was no significant difference observed between middle and high income categories. Therefore, it is not clear if there is a more systematic relationship between income adequacy and asthma occurrence. A much larger sample size of the second cycle of the NPHS allowed us to further explore if the prevalence of asthma increases with decreasing income adequacy among Canadians.
Release date: 2004-09-13 - Articles and reports: 12-001-X20040016996Description:
This article studies the use of the sample distribution for the prediction of finite population totals under single-stage sampling. The proposed predictors employ the sample values of the target study variable, the sampling weights of the sample units and possibly known population values of auxiliary variables. The prediction problem is solved by estimating the expectation of the study values for units outside the sample as a function of the corresponding expectation under the sample distribution and the sampling weights. The prediction mean square error is estimated by a combination of an inverse sampling procedure and a re-sampling method. An interesting outcome of the present analysis is that several familiar estimators in common use are shown to be special cases of the proposed approach, thus providing them a new interpretation. The performance of the new and some old predictors in common use is evaluated and compared by a Monte Carlo simulation study using a real data set.
Release date: 2004-07-14
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Analysis (38)
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- 21. Efficient bootstrap for business surveys ArchivedArticles and reports: 12-001-X200700210494Description:
The Australian Bureau of Statistics has recently developed a generalized estimation system for processing its large scale annual and sub-annual business surveys. Designs for these surveys have a large number of strata, use Simple Random Sampling within Strata, have non-negligible sampling fractions, are overlapping in consecutive periods, and are subject to frame changes. A significant challenge was to choose a variance estimation method that would best meet the following requirements: valid for a wide range of estimators (e.g., ratio and generalized regression), requires limited computation time, can be easily adapted to different designs and estimators, and has good theoretical properties measured in terms of bias and variance. This paper describes the Without Replacement Scaled Bootstrap (WOSB) that was implemented at the ABS and shows that it is appreciably more efficient than the Rao and Wu (1988)'s With Replacement Scaled Bootstrap (WSB). The main advantages of the Bootstrap over alternative replicate variance estimators are its efficiency (i.e., accuracy per unit of storage space) and the relative simplicity with which it can be specified in a system. This paper describes the WOSB variance estimator for point-in-time and movement estimates that can be expressed as a function of finite population means. Simulation results obtained as part of the evaluation process show that the WOSB was more efficient than the WSB, especially when the stratum sample sizes are sometimes as small as 5.
Release date: 2008-01-03 - 22. Mean - Adjusted bootstrap for two - Phase sampling ArchivedArticles 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 - 24. Link-tracing sampling with an initial sample of sites sequentially selected: Estimation of the population size ArchivedArticles and reports: 11-522-X20040018750Description:
This paper modifies the link-tracing sampling with a sequential sample of sites and proposes a maximum likelihood estimator or another one derived under the Bayesian approach. It proposes that confidence intervals be constructed by Bootstrap methods.
Release date: 2005-10-27 - Articles and reports: 12-002-X20050018030Description:
People often wish to use survey micro-data to study whether the rate of occurrence of a particular condition in a subpopulation is the same as the rate of occurrence in the full population. This paper describes some alternatives for making inferences about such a rate difference and shows whether and how these alternatives may be implemented in three different survey software packages. The software packages illustrated - SUDAAN, WesVar and Bootvar - all can make use of bootstrap weights provided by the analyst to carry out variance estimation.
Release date: 2005-06-23 - Articles and reports: 12-002-X20050018031Description:
This article presents revisions to a Stata "bswreg" ado file that calculates variance estimates using bootstrap weights. This revision adds new output and analytic features. The main feature added to the program enables researchers to apply mean bootstrap weights while accounting for the number of weights used to generate the average bootstrap weight. The Workplace and Employee Survey dataset will be used to illustrate the usefulness of this program. This revised version of the "bswreg" command is still an easy to use flexible tool, which is compatible with a wide variety of regression analytical techniques and datasets. The bswreg command and design-based bootstrap weights should only be used for inference when it is theoretically valid.
Release date: 2005-06-23 - Articles and reports: 11-522-X20030017720Description:
This paper examines a jackknife method proposed in Rao (2003) for estimating mean squared errors (MSEs) when generalized linear models or other non-linear models are used for the response of interest. It demonstrate the method's performance in a simulation study.
Release date: 2005-01-26 - 28. Using bootstrap weights with Wes Var and SUDAAN ArchivedArticles and reports: 12-002-X20040027032Description:
This article examines why many Statistics Canada surveys supply bootstrap weights with their microdata for the purpose of design-based variance estimation. Bootstrap weights are not supported by commercially available software such as SUDAAN and WesVar, but there are ways to use these applications to produce boostrap variance estimates.
The paper concludes with a brief discussion of other design-based approaches to variance estimation as well as software, programs and procedures where these methods have been employed.
Release date: 2004-10-05 - Articles and reports: 11-522-X20020016713Description:
This paper explores the relationship between low income and prevalence of asthma. The genetic and environmental determinants are incompletely understood. It has been observed in a previous study that Canadians with low incomes are at increased risk of asthma. Based on data from 17,605 subjects 12 years of age or older who participated in the first cycle of the National Population Health Survey (NPHS) from 1994 to 1995, males and females with low incomes had 1.44- and 1.33-fold increases, respectively, in the prevalence of asthma compared with their counterparts with high incomes. However, there was no significant difference observed between middle and high income categories. Therefore, it is not clear if there is a more systematic relationship between income adequacy and asthma occurrence. A much larger sample size of the second cycle of the NPHS allowed us to further explore if the prevalence of asthma increases with decreasing income adequacy among Canadians.
Release date: 2004-09-13 - Articles and reports: 12-001-X20040016996Description:
This article studies the use of the sample distribution for the prediction of finite population totals under single-stage sampling. The proposed predictors employ the sample values of the target study variable, the sampling weights of the sample units and possibly known population values of auxiliary variables. The prediction problem is solved by estimating the expectation of the study values for units outside the sample as a function of the corresponding expectation under the sample distribution and the sampling weights. The prediction mean square error is estimated by a combination of an inverse sampling procedure and a re-sampling method. An interesting outcome of the present analysis is that several familiar estimators in common use are shown to be special cases of the proposed approach, thus providing them a new interpretation. The performance of the new and some old predictors in common use is evaluated and compared by a Monte Carlo simulation study using a real data set.
Release date: 2004-07-14
Reference (1)
Reference (1) ((1 result))
- Surveys and statistical programs – Documentation: 11-522-X19980015017Description:
Longitudinal studies with repeated observations on individuals permit better characterizations of change and assessment of possible risk factors, but there has been little experience applying sophisticated models for longitudinal data to the complex survey setting. We present results from a comparison of different variance estimation methods for random effects models of change in cognitive function among older adults. The sample design is a stratified sample of people 65 and older, drawn as part of a community-based study designed to examine risk factors for dementia. The model summarizes the population heterogeneity in overall level and rate of change in cognitive function using random effects for intercept and slope. We discuss an unweighted regression including covariates for the stratification variables, a weighted regression, and bootstrapping; we also did preliminary work into using balanced repeated replication and jackknife repeated replication.
Release date: 1999-10-22