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All (269)

All (269) (0 to 10 of 269 results)

1. Canadian Tourism Satellite Account, 2004 Archived
Articles and reports: 13-604-M2009063
Description:
This paper highlights the Canadian Tourism Satellite Account (CTSA) developed by Statistics Canada. The CTSA provides an economic measure of the importance of tourism in terms of expenditures, Gross Domestic Product and employment for Canada. It permits a comparison of tourism with other industries within Canada since the concepts and methods used are based on the framework of the Canadian System of National Accounts. The study revealed that tourism is an important part of Canada's well diversified economy. This paper presents the results of the CTSA for reference year 2004.
This study was prepared by staff of the Research and Development Projects and Analysis Section, Income and Expenditure Accounts Division, Statistics Canada. The study was funded by the Canadian Tourism Commission.
Release date: 2009-12-24
2. Methods for oversampling rare subpopulations in social surveys Archived
Articles and reports: 12-001-X200900211036
Description:
Surveys are frequently required to produce estimates for subpopulations, sometimes for a single subpopulation and sometimes for several subpopulations in addition to the total population. When membership of a rare subpopulation (or domain) can be determined from the sampling frame, selecting the required domain sample size is relatively straightforward. In this case the main issue is the extent of oversampling to employ when survey estimates are required for several domains and for the total population. Sampling and oversampling rare domains whose members cannot be identified in advance present a major challenge. A variety of methods has been used in this situation. In addition to large-scale screening, these methods include disproportionate stratified sampling, two-phase sampling, the use of multiple frames, multiplicity sampling, panel surveys, and the use of multi-purpose surveys. This paper illustrates the application of these methods in a range of social surveys.
Release date: 2009-12-23
3. A standardization of randomized response strategies Archived
Articles and reports: 12-001-X200900211037
Description:
Randomized response strategies, which have originally been developed as statistical methods to reduce nonresponse as well as untruthful answering, can also be applied in the field of statistical disclosure control for public use microdata files. In this paper a standardization of randomized response techniques for the estimation of proportions of identifying or sensitive attributes is presented. The statistical properties of the standardized estimator are derived for general probability sampling. In order to analyse the effect of different choices of the method's implicit "design parameters" on the performance of the estimator we have to include measures of privacy protection in our considerations. These yield variance-optimum design parameters given a certain level of privacy protection. To this end the variables have to be classified into different categories of sensitivity. A real-data example applies the technique in a survey on academic cheating behaviour.
Release date: 2009-12-23
4. Treatments for link nonresponse in indirect sampling Archived
Articles and reports: 12-001-X200900211038
Description:
We examine overcoming the overestimation in using generalized weight share method (GWSM) caused by link nonresponse in indirect sampling. A few adjustment methods incorporating link nonresponse in using GWSM have been constructed for situations both with and without the availability of auxiliary variables. A simulation study on a longitudinal survey is presented using some of the adjustment methods we recommend. The simulation results show that these adjusted GWSMs perform well in reducing both estimation bias and variance. The advancement in bias reduction is significant.
Release date: 2009-12-23
5. Nonparametric propensity weighting for survey nonresponse through local polynomial regression Archived
Articles and reports: 12-001-X200900211039
Description:
Propensity weighting is a procedure to adjust for unit nonresponse in surveys. A form of implementing this procedure consists of dividing the sampling weights by estimates of the probabilities that the sampled units respond to the survey. Typically, these estimates are obtained by fitting parametric models, such as logistic regression. The resulting adjusted estimators may become biased when the specified parametric models are incorrect. To avoid misspecifying such a model, we consider nonparametric estimation of the response probabilities by local polynomial regression. We study the asymptotic properties of the resulting estimator under quasi-randomization. The practical behavior of the proposed nonresponse adjustment approach is evaluated on NHANES data.
Release date: 2009-12-23
6. Estimation of the monthly unemployment rate through structural time series modelling in a rotating panel design Archived
Articles and reports: 12-001-X200900211040
Description:
In this paper a multivariate structural time series model is described that accounts for the panel design of the Dutch Labour Force Survey and is applied to estimate monthly unemployment rates. Compared to the generalized regression estimator, this approach results in a substantial increase of the accuracy due to a reduction of the standard error and the explicit modelling of the bias between the subsequent waves.
Release date: 2009-12-23
7. Estimates for small area compositions subjected to informative missing data Archived
Articles and reports: 12-001-X200900211041
Description:
Estimation of small area (or domain) compositions may suffer from informative missing data, if the probability of missing varies across the categories of interest as well as the small areas. We develop a double mixed modeling approach that combines a random effects mixed model for the underlying complete data with a random effects mixed model of the differential missing-data mechanism. The effect of sampling design can be incorporated through a quasi-likelihood sampling model. The associated conditional mean squared error of prediction is approximated in terms of a three-part decomposition, corresponding to a naive prediction variance, a positive correction that accounts for the hypothetical parameter estimation uncertainty based on the latent complete data, and another positive correction for the extra variation due to the missing data. We illustrate our approach with an application to the estimation of Municipality household compositions based on the Norwegian register household data, which suffer from informative under-registration of the dwelling identity number.
Release date: 2009-12-23
8. Small area population prediction via hierarchical models Archived
Articles and reports: 12-001-X200900211042
Description:
This paper proposes an approach for small area prediction based on data obtained from periodic surveys and censuses. We apply our approach to obtain population predictions for the municipalities not sampled in the Brazilian annual Household Survey (PNAD), as well as to increase the precision of the design-based estimates obtained for the sampled municipalities. In addition to the data provided by the PNAD, we use census demographic data from 1991 and 2000, as well as a complete population count conducted in 1996. Hierarchically non-structured and spatially structured growth models that gain strength from all the sampled municipalities are proposed and compared.
Release date: 2009-12-23
9. Variance estimation in the presence of nonrespondents and certainty strata Archived
Articles and reports: 12-001-X200900211043
Description:
Business surveys often use a one-stage stratified simple random sampling without replacement design with some certainty strata. Although weight adjustment is typically applied for unit nonresponse, the variability due to nonresponse may be omitted in practice when estimating variances. This is problematic especially when there are certainty strata. We derive some variance estimators that are consistent when the number of sampled units in each weighting cell is large, using the jackknife, linearization, and modified jackknife methods. The derived variance estimators are first applied to empirical data from the Annual Capital Expenditures Survey conducted by the U.S. Census Bureau and are then examined in a simulation study.
Release date: 2009-12-23
10. Rescaled bootstrap for stratified multistage sampling Archived
Articles and reports: 12-001-X200900211044
Description:
In large scaled sample surveys it is common practice to employ stratified multistage designs where units are selected using simple random sampling without replacement at each stage. Variance estimation for these types of designs can be quite cumbersome to implement, particularly for non-linear estimators. Various bootstrap methods for variance estimation have been proposed, but most of these are restricted to single-stage designs or two-stage cluster designs. An extension of the rescaled bootstrap method (Rao and Wu 1988) to stratified multistage designs is proposed which can easily be extended to any number of stages. The proposed method is suitable for a wide range of reweighting techniques, including the general class of calibration estimators. A Monte Carlo simulation study was conducted to examine the performance of the proposed multistage rescaled bootstrap variance estimator.
Release date: 2009-12-23

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Analysis (269)

Analysis (269) (0 to 10 of 269 results)

1. Canadian Tourism Satellite Account, 2004 Archived
Articles and reports: 13-604-M2009063
Description:
This paper highlights the Canadian Tourism Satellite Account (CTSA) developed by Statistics Canada. The CTSA provides an economic measure of the importance of tourism in terms of expenditures, Gross Domestic Product and employment for Canada. It permits a comparison of tourism with other industries within Canada since the concepts and methods used are based on the framework of the Canadian System of National Accounts. The study revealed that tourism is an important part of Canada's well diversified economy. This paper presents the results of the CTSA for reference year 2004.
This study was prepared by staff of the Research and Development Projects and Analysis Section, Income and Expenditure Accounts Division, Statistics Canada. The study was funded by the Canadian Tourism Commission.
Release date: 2009-12-24
2. Methods for oversampling rare subpopulations in social surveys Archived
Articles and reports: 12-001-X200900211036
Description:
Surveys are frequently required to produce estimates for subpopulations, sometimes for a single subpopulation and sometimes for several subpopulations in addition to the total population. When membership of a rare subpopulation (or domain) can be determined from the sampling frame, selecting the required domain sample size is relatively straightforward. In this case the main issue is the extent of oversampling to employ when survey estimates are required for several domains and for the total population. Sampling and oversampling rare domains whose members cannot be identified in advance present a major challenge. A variety of methods has been used in this situation. In addition to large-scale screening, these methods include disproportionate stratified sampling, two-phase sampling, the use of multiple frames, multiplicity sampling, panel surveys, and the use of multi-purpose surveys. This paper illustrates the application of these methods in a range of social surveys.
Release date: 2009-12-23
3. A standardization of randomized response strategies Archived
Articles and reports: 12-001-X200900211037
Description:
Randomized response strategies, which have originally been developed as statistical methods to reduce nonresponse as well as untruthful answering, can also be applied in the field of statistical disclosure control for public use microdata files. In this paper a standardization of randomized response techniques for the estimation of proportions of identifying or sensitive attributes is presented. The statistical properties of the standardized estimator are derived for general probability sampling. In order to analyse the effect of different choices of the method's implicit "design parameters" on the performance of the estimator we have to include measures of privacy protection in our considerations. These yield variance-optimum design parameters given a certain level of privacy protection. To this end the variables have to be classified into different categories of sensitivity. A real-data example applies the technique in a survey on academic cheating behaviour.
Release date: 2009-12-23
4. Treatments for link nonresponse in indirect sampling Archived
Articles and reports: 12-001-X200900211038
Description:
We examine overcoming the overestimation in using generalized weight share method (GWSM) caused by link nonresponse in indirect sampling. A few adjustment methods incorporating link nonresponse in using GWSM have been constructed for situations both with and without the availability of auxiliary variables. A simulation study on a longitudinal survey is presented using some of the adjustment methods we recommend. The simulation results show that these adjusted GWSMs perform well in reducing both estimation bias and variance. The advancement in bias reduction is significant.
Release date: 2009-12-23
5. Nonparametric propensity weighting for survey nonresponse through local polynomial regression Archived
Articles and reports: 12-001-X200900211039
Description:
Propensity weighting is a procedure to adjust for unit nonresponse in surveys. A form of implementing this procedure consists of dividing the sampling weights by estimates of the probabilities that the sampled units respond to the survey. Typically, these estimates are obtained by fitting parametric models, such as logistic regression. The resulting adjusted estimators may become biased when the specified parametric models are incorrect. To avoid misspecifying such a model, we consider nonparametric estimation of the response probabilities by local polynomial regression. We study the asymptotic properties of the resulting estimator under quasi-randomization. The practical behavior of the proposed nonresponse adjustment approach is evaluated on NHANES data.
Release date: 2009-12-23
6. Estimation of the monthly unemployment rate through structural time series modelling in a rotating panel design Archived
Articles and reports: 12-001-X200900211040
Description:
In this paper a multivariate structural time series model is described that accounts for the panel design of the Dutch Labour Force Survey and is applied to estimate monthly unemployment rates. Compared to the generalized regression estimator, this approach results in a substantial increase of the accuracy due to a reduction of the standard error and the explicit modelling of the bias between the subsequent waves.
Release date: 2009-12-23
7. Estimates for small area compositions subjected to informative missing data Archived
Articles and reports: 12-001-X200900211041
Description:
Estimation of small area (or domain) compositions may suffer from informative missing data, if the probability of missing varies across the categories of interest as well as the small areas. We develop a double mixed modeling approach that combines a random effects mixed model for the underlying complete data with a random effects mixed model of the differential missing-data mechanism. The effect of sampling design can be incorporated through a quasi-likelihood sampling model. The associated conditional mean squared error of prediction is approximated in terms of a three-part decomposition, corresponding to a naive prediction variance, a positive correction that accounts for the hypothetical parameter estimation uncertainty based on the latent complete data, and another positive correction for the extra variation due to the missing data. We illustrate our approach with an application to the estimation of Municipality household compositions based on the Norwegian register household data, which suffer from informative under-registration of the dwelling identity number.
Release date: 2009-12-23
8. Small area population prediction via hierarchical models Archived
Articles and reports: 12-001-X200900211042
Description:
This paper proposes an approach for small area prediction based on data obtained from periodic surveys and censuses. We apply our approach to obtain population predictions for the municipalities not sampled in the Brazilian annual Household Survey (PNAD), as well as to increase the precision of the design-based estimates obtained for the sampled municipalities. In addition to the data provided by the PNAD, we use census demographic data from 1991 and 2000, as well as a complete population count conducted in 1996. Hierarchically non-structured and spatially structured growth models that gain strength from all the sampled municipalities are proposed and compared.
Release date: 2009-12-23
9. Variance estimation in the presence of nonrespondents and certainty strata Archived
Articles and reports: 12-001-X200900211043
Description:
Business surveys often use a one-stage stratified simple random sampling without replacement design with some certainty strata. Although weight adjustment is typically applied for unit nonresponse, the variability due to nonresponse may be omitted in practice when estimating variances. This is problematic especially when there are certainty strata. We derive some variance estimators that are consistent when the number of sampled units in each weighting cell is large, using the jackknife, linearization, and modified jackknife methods. The derived variance estimators are first applied to empirical data from the Annual Capital Expenditures Survey conducted by the U.S. Census Bureau and are then examined in a simulation study.
Release date: 2009-12-23
10. Rescaled bootstrap for stratified multistage sampling Archived
Articles and reports: 12-001-X200900211044
Description:
In large scaled sample surveys it is common practice to employ stratified multistage designs where units are selected using simple random sampling without replacement at each stage. Variance estimation for these types of designs can be quite cumbersome to implement, particularly for non-linear estimators. Various bootstrap methods for variance estimation have been proposed, but most of these are restricted to single-stage designs or two-stage cluster designs. An extension of the rescaled bootstrap method (Rao and Wu 1988) to stratified multistage designs is proposed which can easily be extended to any number of stages. The proposed method is suitable for a wide range of reweighting techniques, including the general class of calibration estimators. A Monte Carlo simulation study was conducted to examine the performance of the proposed multistage rescaled bootstrap variance estimator.
Release date: 2009-12-23

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Date modified:: 2024-07-28