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  • 1. A New Understanding of Post-secondary Education in Canada: A Discussion Paper Archived
    Articles and reports: 81-595-M2003011
    Geography: Canada
    Description:

    This report presents a rethinking of the fundamental concepts used to guide statistical work on postsecondary education.

    Release date: 2003-12-23
  • 2. Methodological issues in the development and use of statistical indicators for International comparisons Archived
    Articles and reports: 12-001-X20030016600
    Description:

    International comparability of Official Statistics is important for domestic uses within any country. But international comparability matters also for the international uses of statistics; in particular the development and monitoring of global policies and assessing economic and social development throughout the world. Additionally statistics are used by international agencies and bilateral technical assistance programmes to monitor the impact of technical assistance.The first part of this paper describes how statistical indicators are used by the United Nations and other agencies. The framework of statistical indicators for these purposes is described ans some issues concerning the choice and quality of these indicators are identified.In the past there has been considerable methodological research in support of Official Statistics particularly by the strongest National Statistical Offices and some academics. This has established the basic methodologies for Official Statistics and has led to considerable developments and quality improvements over time. Much has been achieved. However the focus has, to an extent, been on national uses of Official Statistics. These developments have, of course, benefited the international uses, and some specific developments have also occurred. There is however a need to foster more methodological development on the international requirements. In the second part of this paper a number of examples illustrate this need.

    Release date: 2003-07-31
  • 3. A noninformative Bayesian approach to small area estimation Archived
    Articles and reports: 12-001-X20030016601
    Description:

    In small area estimation, one uses data from similar domains to estimate the mean in a particular small area. This borrowing of strength is justified by assuming a model that relates the small area means. Here, we suggest a non-informative or objective Bayesian approach to small area estimation. Using this approach, one can estimate population parameters other than means and find sensible estimates of their precision. AMS 1991 subject classifications Primary 62D05; secondary 62C10.

    Release date: 2003-07-31
  • 4. Model-based unemployment rate estimation for the Canadian Labour Force Survey: A hierarchical Bayes approach Archived
    Articles and reports: 12-001-X20030016602
    Description:

    The Canadian Labour Force Survey (LFS) produces monthly direct estimates of the unemployment rate at national and provincial levels. The LFS also releases unemployment estimates for subprovincial areas such as census metropolitan areas (CMAs) and census agglomerations (CAs). However, for some subprovincial areas, the direct estimates are not very reliable since the sample size in some areas is quite small. In this paper, a cross-sectional and time-series model is used to borrow strength across areas and time periods to produce model-based unemployment rate estimates for CMAs and CAs. This model is a generalization of a widely used cross-sectional model in small area estimation and includes a random walk or AR(1) model for the random time component. Monthly Employment Insurance (EI) beneficiary data at the CMA or CA level are used as auxiliary covariates in the model. A hierarchical Bayes (HB) approach is employed and the Gibbs sampler is used to generate samples from the joint posterior distribution. Rao-Blackwellized estimators are obtained for the posterior means and posterior variances of the CMA/CA-level unemployment rates. The HB method smoothes the survey estimates and leads to a substantial reduction in standard errors. Base on posterior distributions, bayesian model fitting is also investigated in this paper.

    Release date: 2003-07-31
  • 5. The effect of model choice in estimation for domains, including small domains Archived
    Articles and reports: 12-001-X20030016605
    Description:

    In this paper, we examine the effects of model choice on different types of estimators for totals of domains (including small domains or small areas) for a sampled finite population. The paper asks how different estimator types compare for a common underlying model statement. We argue that estimator type - synthetic, generalized regression (GREG), composite, empirical best linear unbiased predicition (EBLUP), hierarchical Bayes, and so on - is one important aspect of domain estimation, and that the choice of the model, including its parameters and effects, is a second aspect, conceptually different from the first. Earlier work has not always made this distinction clear. For a given estimator type, one can derive different estimators, depending on the choice of model. In recent literature, a number of estimator types have been proposed, but there is relatively little impartial comparisons made among them. In this paper, we discuss three types: synthetic, GREG, and, to a limited extent, composite. We show that model improvement - the transition from a weaker to a stronger model - has very different effects on the different estimator types. We also show that the difference in accuracy between the different estimator types depends on the choice of model. For a well-specified model, the difference in accuracy between synthetic and GREG is negligible, but it can be substantial if the model is mis-specified. The synthetic type then tends to be highly inaccurate. We rely partly on theoretical results (for simple random sampling only) and partly on empirical results. The empirical results are based on simulations with repeated samples drawn from two finite populations, one artificially constructed, the other constructed from the real data of the Finnish Labour Force Survey.

    Release date: 2003-07-31
  • 6. Adjustment of unemployment estimates based on small area estimation in Korea Archived
    Articles and reports: 12-001-X20030016607
    Description:

    The Korean Economically Active Population Survey (EAPS) has been conducted in order to produce unemployment statistics for large areas such as metropolitan cities and provincial levels. Large areas have been designated as planned domains in the EAPS and local self-government areas (LSGAs) as unplanned domains. In this study, we suggest small area estimation methods to adjust for the unemployment statistics of LSGAs within large areas estimated directly from current EAPS data. We suggest synthetic and composite estimators under the Korean EAPS system, and for the model-based estimator we put forward the hierarchical Bayes (HB) estimator from the general multi-level model. The HB estimator we use here was introduced by You and Rao (2000). The mean square errors of the synthetic and composite estimates are derived from the EAPS data by the Jackknife method, and are used as a measure of accuracy for the small area estimates. Gibbs sampling is used to obtain the HB estimates and their posterior variances, which we use to measure precision for small area estimates. The total unemployment figures of the 10 LSGAs within the ChoongBuk Province produced by the December 2000 EAPS data have been estimated using the small area estimation methods suggested in this study. The reliability of small area estimates is evaluated by the relative standard errors or the relative root mean square errors of these estimates. Here, under the current Korean EAPS system, we suggest that the composite estimates are more reliable than other small area estimates.

    Release date: 2003-07-31
  • 7. Conditional and unconditional analysis of some small area estimators in complex sampling Archived
    Articles and reports: 12-001-X20030016608
    Description:

    This work deals with the unconditional and conditional properties of some well-known small area estimators: expansion, post-stratified ratio, synthetic, composite, sample size dependent and the empirical best linear unbiased predictor (EBLUP). A two-stage sampling design is considered as it is commonly used in household surveys conducted by the National Statistics Institute of Italy. An evaluation is carried out through a simulation based on 1991 Italian census data. The small areas considered are the local labour market areas, which are unplanned domains that cut across the boundaries of the design strata.

    Release date: 2003-07-31
  • 8. Solving the error localization problem by means of vertex generation Archived
    Articles and reports: 12-001-X20030016609
    Description:

    To automate the data editing process the so-called error localization problem, i.e., the problem of identifying the erroneous fields in an erroneous record, has to be solved. A paradigm for identifying errors automatically has been proposed by Fellegi and Holt in 1976. Over the years their paradigm has been generalized to: the data of a record should be made to satisfy all edits by changing the values of the variables with the smallest possible sum of reliability weights. A reliability weight of a variable is a non-negative number that expresses how reliable one considers the value of this variable to be. Given this paradigm the resulting mathematical problem has to be solved. In the present paper we examine how vertex generation methods can be used to solve this mathematical problem in mixed data, i.e., a combination of categorical (discrete) and numerical (continuous) data. The main aim of this paper is not to present new results, but rather to combine the ideas of several other papers in order to give a "complete", self-contained description of the use of vertex generation methods to solve the error localization problem in mixed data. In our exposition we will focus on describing how methods for numerical data can be adapted to mixed data.

    Release date: 2003-07-31
  • 9. Inference for population means under unweighted imputation for missing survey data Archived
    Articles and reports: 12-001-X20030016610
    Description:

    In the presence of item nonreponse, unweighted imputation methods are often used in practice but they generally lead to biased estimators under uniform response within imputation classes. Following Skinner and Rao (2002), we propose a bias-adjusted estimator of a population mean under unweighted ratio imputation and random hot-deck imputation and derive linearization variance estimators. A small simulation study is conducted to study the performance of the methods in terms of bias and mean square error. Relative bias and relative stability of the variance estimators are also studied.

    Release date: 2003-07-31
  • 10. Minimum risk, fixed cost sampling designs for independent Poisson processes Archived
    Articles and reports: 12-001-X20030016611
    Description:

    Optimal and approximately optimal fixed-cost Bayesian sampling designs are considered for simultaneous estimation in independent homogeneous Poisson processes. General allocation formulae are developed for a basic Poisson-Gamma model and these are compared with more traditional allocation methods. Techniques for finding representative gamma priors under more general hierarchical models are also discussed. The techniques show that, in many practical situations, these gamma priors provide reasonable approximations to the hierarchical prior and Bayes risk. The methods developed are general enough to apply to a wide variety of models and are not limited to Poisson processes.

    Release date: 2003-07-31
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Articles and reports (29)

Articles and reports (29) (0 to 10 of 29 results)

  • 1. A New Understanding of Post-secondary Education in Canada: A Discussion Paper Archived
    Articles and reports: 81-595-M2003011
    Geography: Canada
    Description:

    This report presents a rethinking of the fundamental concepts used to guide statistical work on postsecondary education.

    Release date: 2003-12-23
  • 2. Methodological issues in the development and use of statistical indicators for International comparisons Archived
    Articles and reports: 12-001-X20030016600
    Description:

    International comparability of Official Statistics is important for domestic uses within any country. But international comparability matters also for the international uses of statistics; in particular the development and monitoring of global policies and assessing economic and social development throughout the world. Additionally statistics are used by international agencies and bilateral technical assistance programmes to monitor the impact of technical assistance.The first part of this paper describes how statistical indicators are used by the United Nations and other agencies. The framework of statistical indicators for these purposes is described ans some issues concerning the choice and quality of these indicators are identified.In the past there has been considerable methodological research in support of Official Statistics particularly by the strongest National Statistical Offices and some academics. This has established the basic methodologies for Official Statistics and has led to considerable developments and quality improvements over time. Much has been achieved. However the focus has, to an extent, been on national uses of Official Statistics. These developments have, of course, benefited the international uses, and some specific developments have also occurred. There is however a need to foster more methodological development on the international requirements. In the second part of this paper a number of examples illustrate this need.

    Release date: 2003-07-31
  • 3. A noninformative Bayesian approach to small area estimation Archived
    Articles and reports: 12-001-X20030016601
    Description:

    In small area estimation, one uses data from similar domains to estimate the mean in a particular small area. This borrowing of strength is justified by assuming a model that relates the small area means. Here, we suggest a non-informative or objective Bayesian approach to small area estimation. Using this approach, one can estimate population parameters other than means and find sensible estimates of their precision. AMS 1991 subject classifications Primary 62D05; secondary 62C10.

    Release date: 2003-07-31
  • 4. Model-based unemployment rate estimation for the Canadian Labour Force Survey: A hierarchical Bayes approach Archived
    Articles and reports: 12-001-X20030016602
    Description:

    The Canadian Labour Force Survey (LFS) produces monthly direct estimates of the unemployment rate at national and provincial levels. The LFS also releases unemployment estimates for subprovincial areas such as census metropolitan areas (CMAs) and census agglomerations (CAs). However, for some subprovincial areas, the direct estimates are not very reliable since the sample size in some areas is quite small. In this paper, a cross-sectional and time-series model is used to borrow strength across areas and time periods to produce model-based unemployment rate estimates for CMAs and CAs. This model is a generalization of a widely used cross-sectional model in small area estimation and includes a random walk or AR(1) model for the random time component. Monthly Employment Insurance (EI) beneficiary data at the CMA or CA level are used as auxiliary covariates in the model. A hierarchical Bayes (HB) approach is employed and the Gibbs sampler is used to generate samples from the joint posterior distribution. Rao-Blackwellized estimators are obtained for the posterior means and posterior variances of the CMA/CA-level unemployment rates. The HB method smoothes the survey estimates and leads to a substantial reduction in standard errors. Base on posterior distributions, bayesian model fitting is also investigated in this paper.

    Release date: 2003-07-31
  • 5. The effect of model choice in estimation for domains, including small domains Archived
    Articles and reports: 12-001-X20030016605
    Description:

    In this paper, we examine the effects of model choice on different types of estimators for totals of domains (including small domains or small areas) for a sampled finite population. The paper asks how different estimator types compare for a common underlying model statement. We argue that estimator type - synthetic, generalized regression (GREG), composite, empirical best linear unbiased predicition (EBLUP), hierarchical Bayes, and so on - is one important aspect of domain estimation, and that the choice of the model, including its parameters and effects, is a second aspect, conceptually different from the first. Earlier work has not always made this distinction clear. For a given estimator type, one can derive different estimators, depending on the choice of model. In recent literature, a number of estimator types have been proposed, but there is relatively little impartial comparisons made among them. In this paper, we discuss three types: synthetic, GREG, and, to a limited extent, composite. We show that model improvement - the transition from a weaker to a stronger model - has very different effects on the different estimator types. We also show that the difference in accuracy between the different estimator types depends on the choice of model. For a well-specified model, the difference in accuracy between synthetic and GREG is negligible, but it can be substantial if the model is mis-specified. The synthetic type then tends to be highly inaccurate. We rely partly on theoretical results (for simple random sampling only) and partly on empirical results. The empirical results are based on simulations with repeated samples drawn from two finite populations, one artificially constructed, the other constructed from the real data of the Finnish Labour Force Survey.

    Release date: 2003-07-31
  • 6. Adjustment of unemployment estimates based on small area estimation in Korea Archived
    Articles and reports: 12-001-X20030016607
    Description:

    The Korean Economically Active Population Survey (EAPS) has been conducted in order to produce unemployment statistics for large areas such as metropolitan cities and provincial levels. Large areas have been designated as planned domains in the EAPS and local self-government areas (LSGAs) as unplanned domains. In this study, we suggest small area estimation methods to adjust for the unemployment statistics of LSGAs within large areas estimated directly from current EAPS data. We suggest synthetic and composite estimators under the Korean EAPS system, and for the model-based estimator we put forward the hierarchical Bayes (HB) estimator from the general multi-level model. The HB estimator we use here was introduced by You and Rao (2000). The mean square errors of the synthetic and composite estimates are derived from the EAPS data by the Jackknife method, and are used as a measure of accuracy for the small area estimates. Gibbs sampling is used to obtain the HB estimates and their posterior variances, which we use to measure precision for small area estimates. The total unemployment figures of the 10 LSGAs within the ChoongBuk Province produced by the December 2000 EAPS data have been estimated using the small area estimation methods suggested in this study. The reliability of small area estimates is evaluated by the relative standard errors or the relative root mean square errors of these estimates. Here, under the current Korean EAPS system, we suggest that the composite estimates are more reliable than other small area estimates.

    Release date: 2003-07-31
  • 7. Conditional and unconditional analysis of some small area estimators in complex sampling Archived
    Articles and reports: 12-001-X20030016608
    Description:

    This work deals with the unconditional and conditional properties of some well-known small area estimators: expansion, post-stratified ratio, synthetic, composite, sample size dependent and the empirical best linear unbiased predictor (EBLUP). A two-stage sampling design is considered as it is commonly used in household surveys conducted by the National Statistics Institute of Italy. An evaluation is carried out through a simulation based on 1991 Italian census data. The small areas considered are the local labour market areas, which are unplanned domains that cut across the boundaries of the design strata.

    Release date: 2003-07-31
  • 8. Solving the error localization problem by means of vertex generation Archived
    Articles and reports: 12-001-X20030016609
    Description:

    To automate the data editing process the so-called error localization problem, i.e., the problem of identifying the erroneous fields in an erroneous record, has to be solved. A paradigm for identifying errors automatically has been proposed by Fellegi and Holt in 1976. Over the years their paradigm has been generalized to: the data of a record should be made to satisfy all edits by changing the values of the variables with the smallest possible sum of reliability weights. A reliability weight of a variable is a non-negative number that expresses how reliable one considers the value of this variable to be. Given this paradigm the resulting mathematical problem has to be solved. In the present paper we examine how vertex generation methods can be used to solve this mathematical problem in mixed data, i.e., a combination of categorical (discrete) and numerical (continuous) data. The main aim of this paper is not to present new results, but rather to combine the ideas of several other papers in order to give a "complete", self-contained description of the use of vertex generation methods to solve the error localization problem in mixed data. In our exposition we will focus on describing how methods for numerical data can be adapted to mixed data.

    Release date: 2003-07-31
  • 9. Inference for population means under unweighted imputation for missing survey data Archived
    Articles and reports: 12-001-X20030016610
    Description:

    In the presence of item nonreponse, unweighted imputation methods are often used in practice but they generally lead to biased estimators under uniform response within imputation classes. Following Skinner and Rao (2002), we propose a bias-adjusted estimator of a population mean under unweighted ratio imputation and random hot-deck imputation and derive linearization variance estimators. A small simulation study is conducted to study the performance of the methods in terms of bias and mean square error. Relative bias and relative stability of the variance estimators are also studied.

    Release date: 2003-07-31
  • 10. Minimum risk, fixed cost sampling designs for independent Poisson processes Archived
    Articles and reports: 12-001-X20030016611
    Description:

    Optimal and approximately optimal fixed-cost Bayesian sampling designs are considered for simultaneous estimation in independent homogeneous Poisson processes. General allocation formulae are developed for a basic Poisson-Gamma model and these are compared with more traditional allocation methods. Techniques for finding representative gamma priors under more general hierarchical models are also discussed. The techniques show that, in many practical situations, these gamma priors provide reasonable approximations to the hierarchical prior and Bayes risk. The methods developed are general enough to apply to a wide variety of models and are not limited to Poisson processes.

    Release date: 2003-07-31
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