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1. A multivariate procedure towards composite estimation of consumer expenditure for the U.S. consumer price index numbers Archived
Articles and reports: 12-001-X199200214480
Description:
We consider the problem of estimating the “cost weights” and “relative importances” of different item strata for the local market basket areas. The estimation of these parameters is...
Description:
We consider the problem of estimating the “cost weights” and “relative importances” of different item strata for the local market basket areas. The estimation of these parameters is needed to construct the U.S. Consumer Price Index Numbers. We use multivariate models to construct composite estimators which combine information from relevant sources. The mean squared errors (MSE) of the proposed and the existing estimators are estimated using the repeated half samples available from the survey. Based on our numerical results, the proposed estimators seem to be superior to the existing estimators.
More
Release date: 1992-12-15
2. The role of the interviewer in survey participation Archived
Articles and reports: 12-001-X199200214481
Description:
Using data from a survey of U.S. Census Bureau interviewers, this paper examines whether experienced interviewers achieve higher response rates than inexperienced interviewers...
Description:
Using data from a survey of U.S. Census Bureau interviewers, this paper examines whether experienced interviewers achieve higher response rates than inexperienced interviewers, controlling for differences in survey design and attributes of the populations assigned to them. After demonstrating that the relationship is positive and curvilinear, it attempts to explain the mechanisms by which experienced interviewers achieve these rates and elaborate the nature of the relationship. It examines what behaviors and attitudes underlie the higher success, with the hope that they might be instilled in trainees.
More
Release date: 1992-12-15
3. A sample allocation method for two-phase survey designs Archived
Articles and reports: 12-001-X199200214482
Description:
Motivated by a business survey design at Statistics Canada, we formulate the problem of sample allocation for a general two-phase survey design as a constrained nonlinear programming...
Description:
Motivated by a business survey design at Statistics Canada, we formulate the problem of sample allocation for a general two-phase survey design as a constrained nonlinear programming problem. By exploiting its mathematical structure, we propose a solution method that consists of iterations between two subproblems that are computationally much simpler. Using an approximate solution as a starting value, the proposed method works very well in an empirical study.
More
Release date: 1992-12-15
4. Methods for estimating the precision of survey estimates when imputation has been used Archived
Articles and reports: 12-001-X199200214483
Description:
In almost all large surveys, some form of imputation is used. This paper develops a method for variance estimation when single (as opposed to multiple) imputation is used to create a...
Description:
In almost all large surveys, some form of imputation is used. This paper develops a method for variance estimation when single (as opposed to multiple) imputation is used to create a completed data set. Imputation will never reproduce the true values (except in truly exceptional cases). The total error of the survey estimate is viewed in this paper as the sum of sampling error and imputation error. Consequently, an overall variance is derived as the sum of a sampling variance and an imputation variance. The principal theme is the estimation of these two components, using the data after imputation, that is, the actually observed values and the imputed values. The approach is model assisted in the sense that the model implied by the imputation method and the randomization distribution used for sample selection will together determine the appearance of the variance estimators. The theoretical findings are confirmed by a Monte Carlo simulation.
More
Release date: 1992-12-15
5. Maximum likelihood estimation from complex sample surveys Archived
Articles and reports: 12-001-X199200214484
Description:
Maximum likelihood estimation from complex sample data requires additional modeling due to the information in the sample selection. Alternatively, pseudo maximum likelihood methods...
Description:
Maximum likelihood estimation from complex sample data requires additional modeling due to the information in the sample selection. Alternatively, pseudo maximum likelihood methods that consist of maximizing estimates of the census score function can be applied. In this article we review some of the approaches considered in the literature and compare them with a new approach derived from the ideas of ‘weighted distributions’. The focus of the comparisons is on situations where some or all of the design variables are unknown or misspecified. The results obtained for the new method are encouraging, but the study is limited so far to simple situations.
More
Release date: 1992-12-15
6. An estimating function approach to finite population estimation Archived
Articles and reports: 12-001-X199200214485
Description:
Godambe and Thompson (1986) define and develop simultaneous optimal estimation of superpopulation and finite population parameters based on a superpopulation model and a survey...
Description:
Godambe and Thompson (1986) define and develop simultaneous optimal estimation of superpopulation and finite population parameters based on a superpopulation model and a survey sampling design. Their theory defines the finite population parameter, \theta_N, as the solution of the optimal estimating equation for the superpopulation parameter \theta; however, some other finite population parameter, \phi, may be of interest. We propose to extend the superpopulation model in such a way that the parameter of interest, \phi, is a known function of \theta_N, say \phi = f (\theta_N). Then \phi is optimally estimated by f (\theta_s), where \theta_s is the optimal estimator of \theta_N, as given by Godambe and Thompson (1986), based on the sample s and the sampling design.
More
Release date: 1992-12-15
7. Some recent work on resampling methods for complex surveys Archived
Articles and reports: 12-001-X199200214486
Description:
Resampling methods for inference with complex survey data include the jackknife, balanced repeated replication (BRR) and the bootstrap. We review some recent work on these methods for...
Description:
Resampling methods for inference with complex survey data include the jackknife, balanced repeated replication (BRR) and the bootstrap. We review some recent work on these methods for standard error and confidence interval estimation. Some empirical results for non-smooth statistics are also given.
More
Release date: 1992-12-15
8. Robust model-based methods for analytic surveys Archived
Articles and reports: 12-001-X199200214487
Description:
This paper reviews the idea of robustness for randomisation and model-based inference for descriptive and analytic surveys. The lack of robustness for model-based procedures can be...
Description:
This paper reviews the idea of robustness for randomisation and model-based inference for descriptive and analytic surveys. The lack of robustness for model-based procedures can be partially overcome by careful design. In this paper a robust model-based approach to analysis is proposed based on smoothing methods.
More
Release date: 1992-12-15
9. Robustness and optimal design under prediction models for finite populations Archived
Articles and reports: 12-001-X199200214488
Description:
In many finite population sampling problems the design that is optimal in the sense of minimizing the variance of the best linear unbiased estimator under a particular working model is...
Description:
In many finite population sampling problems the design that is optimal in the sense of minimizing the variance of the best linear unbiased estimator under a particular working model is bad in the sense of robustness - it leaves the estimator extremely vulnerable to bias if the working model is incorrect. However there are some important models under which one design provides both efficiency and robustness. We present a theorem that identifies such models and their optimal designs.
More
Release date: 1992-12-15
10. Job-related moves Archived
Articles and reports: 75-001-X1992004101
Geography: Canada
Description:
The characteristics of people who moved in 1987 to look for work, start a new job or take a transfer are profiled.
Release date: 1992-12-01

Articles and reports (35) (0 to 10 of 35 results)

1. A multivariate procedure towards composite estimation of consumer expenditure for the U.S. consumer price index numbers Archived
Articles and reports: 12-001-X199200214480
Description:
We consider the problem of estimating the “cost weights” and “relative importances” of different item strata for the local market basket areas. The estimation of these parameters is...
Description:
We consider the problem of estimating the “cost weights” and “relative importances” of different item strata for the local market basket areas. The estimation of these parameters is needed to construct the U.S. Consumer Price Index Numbers. We use multivariate models to construct composite estimators which combine information from relevant sources. The mean squared errors (MSE) of the proposed and the existing estimators are estimated using the repeated half samples available from the survey. Based on our numerical results, the proposed estimators seem to be superior to the existing estimators.
More
Release date: 1992-12-15
2. The role of the interviewer in survey participation Archived
Articles and reports: 12-001-X199200214481
Description:
Using data from a survey of U.S. Census Bureau interviewers, this paper examines whether experienced interviewers achieve higher response rates than inexperienced interviewers...
Description:
Using data from a survey of U.S. Census Bureau interviewers, this paper examines whether experienced interviewers achieve higher response rates than inexperienced interviewers, controlling for differences in survey design and attributes of the populations assigned to them. After demonstrating that the relationship is positive and curvilinear, it attempts to explain the mechanisms by which experienced interviewers achieve these rates and elaborate the nature of the relationship. It examines what behaviors and attitudes underlie the higher success, with the hope that they might be instilled in trainees.
More
Release date: 1992-12-15
3. A sample allocation method for two-phase survey designs Archived
Articles and reports: 12-001-X199200214482
Description:
Motivated by a business survey design at Statistics Canada, we formulate the problem of sample allocation for a general two-phase survey design as a constrained nonlinear programming...
Description:
Motivated by a business survey design at Statistics Canada, we formulate the problem of sample allocation for a general two-phase survey design as a constrained nonlinear programming problem. By exploiting its mathematical structure, we propose a solution method that consists of iterations between two subproblems that are computationally much simpler. Using an approximate solution as a starting value, the proposed method works very well in an empirical study.
More
Release date: 1992-12-15
4. Methods for estimating the precision of survey estimates when imputation has been used Archived
Articles and reports: 12-001-X199200214483
Description:
In almost all large surveys, some form of imputation is used. This paper develops a method for variance estimation when single (as opposed to multiple) imputation is used to create a...
Description:
In almost all large surveys, some form of imputation is used. This paper develops a method for variance estimation when single (as opposed to multiple) imputation is used to create a completed data set. Imputation will never reproduce the true values (except in truly exceptional cases). The total error of the survey estimate is viewed in this paper as the sum of sampling error and imputation error. Consequently, an overall variance is derived as the sum of a sampling variance and an imputation variance. The principal theme is the estimation of these two components, using the data after imputation, that is, the actually observed values and the imputed values. The approach is model assisted in the sense that the model implied by the imputation method and the randomization distribution used for sample selection will together determine the appearance of the variance estimators. The theoretical findings are confirmed by a Monte Carlo simulation.
More
Release date: 1992-12-15
5. Maximum likelihood estimation from complex sample surveys Archived
Articles and reports: 12-001-X199200214484
Description:
Maximum likelihood estimation from complex sample data requires additional modeling due to the information in the sample selection. Alternatively, pseudo maximum likelihood methods...
Description:
Maximum likelihood estimation from complex sample data requires additional modeling due to the information in the sample selection. Alternatively, pseudo maximum likelihood methods that consist of maximizing estimates of the census score function can be applied. In this article we review some of the approaches considered in the literature and compare them with a new approach derived from the ideas of ‘weighted distributions’. The focus of the comparisons is on situations where some or all of the design variables are unknown or misspecified. The results obtained for the new method are encouraging, but the study is limited so far to simple situations.
More
Release date: 1992-12-15
6. An estimating function approach to finite population estimation Archived
Articles and reports: 12-001-X199200214485
Description:
Godambe and Thompson (1986) define and develop simultaneous optimal estimation of superpopulation and finite population parameters based on a superpopulation model and a survey...
Description:
Godambe and Thompson (1986) define and develop simultaneous optimal estimation of superpopulation and finite population parameters based on a superpopulation model and a survey sampling design. Their theory defines the finite population parameter, \theta_N, as the solution of the optimal estimating equation for the superpopulation parameter \theta; however, some other finite population parameter, \phi, may be of interest. We propose to extend the superpopulation model in such a way that the parameter of interest, \phi, is a known function of \theta_N, say \phi = f (\theta_N). Then \phi is optimally estimated by f (\theta_s), where \theta_s is the optimal estimator of \theta_N, as given by Godambe and Thompson (1986), based on the sample s and the sampling design.
More
Release date: 1992-12-15
7. Some recent work on resampling methods for complex surveys Archived
Articles and reports: 12-001-X199200214486
Description:
Resampling methods for inference with complex survey data include the jackknife, balanced repeated replication (BRR) and the bootstrap. We review some recent work on these methods for...
Description:
Resampling methods for inference with complex survey data include the jackknife, balanced repeated replication (BRR) and the bootstrap. We review some recent work on these methods for standard error and confidence interval estimation. Some empirical results for non-smooth statistics are also given.
More
Release date: 1992-12-15
8. Robust model-based methods for analytic surveys Archived
Articles and reports: 12-001-X199200214487
Description:
This paper reviews the idea of robustness for randomisation and model-based inference for descriptive and analytic surveys. The lack of robustness for model-based procedures can be...
Description:
This paper reviews the idea of robustness for randomisation and model-based inference for descriptive and analytic surveys. The lack of robustness for model-based procedures can be partially overcome by careful design. In this paper a robust model-based approach to analysis is proposed based on smoothing methods.
More
Release date: 1992-12-15
9. Robustness and optimal design under prediction models for finite populations Archived
Articles and reports: 12-001-X199200214488
Description:
In many finite population sampling problems the design that is optimal in the sense of minimizing the variance of the best linear unbiased estimator under a particular working model is...
Description:
In many finite population sampling problems the design that is optimal in the sense of minimizing the variance of the best linear unbiased estimator under a particular working model is bad in the sense of robustness - it leaves the estimator extremely vulnerable to bias if the working model is incorrect. However there are some important models under which one design provides both efficiency and robustness. We present a theorem that identifies such models and their optimal designs.
More
Release date: 1992-12-15
10. Job-related moves Archived
Articles and reports: 75-001-X1992004101
Geography: Canada
Description:
The characteristics of people who moved in 1987 to look for work, start a new job or take a transfer are profiled.
Release date: 1992-12-01

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