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All (15) (0 to 10 of 15 results)

  • 1. Application of sampling variance smoothing methods for small area proportion estimation Archived
    Articles and reports: 11-522-X202200100005
    Description: Sampling variance smoothing is an important topic in small area estimation. In this paper, we propose sampling variance smoothing methods for small area proportion estimation. In particular, we consider the generalized variance function and design effect methods for sampling variance smoothing. We evaluate and compare the smoothed sampling variances and small area estimates based on the smoothed variance estimates through analysis of survey data from Statistics Canada. The results from real data analysis indicate that the proposed sampling variance smoothing methods work very well for small area estimation.
    Release date: 2024-03-25
  • 2. Non-response follow-up for business surveys
    Articles and reports: 12-001-X202200100006
    Description:

    In the last two decades, survey response rates have been steadily falling. In that context, it has become increasingly important for statistical agencies to develop and use methods that reduce the adverse effects of non-response on the accuracy of survey estimates. Follow-up of non-respondents may be an effective, albeit time and resource-intensive, remedy for non-response bias. We conducted a simulation study using real business survey data to shed some light on several questions about non-response follow-up. For instance, assuming a fixed non-response follow-up budget, what is the best way to select non-responding units to be followed up? How much effort should be dedicated to repeatedly following up non-respondents until a response is received? Should they all be followed up or a sample of them? If a sample is followed up, how should it be selected? We compared Monte Carlo relative biases and relative root mean square errors under different follow-up sampling designs, sample sizes and non-response scenarios. We also determined an expression for the minimum follow-up sample size required to expend the budget, on average, and showed that it maximizes the expected response rate. A main conclusion of our simulation experiment is that this sample size also appears to approximately minimize the bias and mean square error of the estimates.

    Release date: 2022-06-21
  • 3. Comparison of unit level and area level small area estimators Archived
    Articles and reports: 12-001-X201600114540
    Description:

    In this paper, we compare the EBLUP and pseudo-EBLUP estimators for small area estimation under the nested error regression model and three area level model-based estimators using the Fay-Herriot model. We conduct a design-based simulation study to compare the model-based estimators for unit level and area level models under informative and non-informative sampling. In particular, we are interested in the confidence interval coverage rate of the unit level and area level estimators. We also compare the estimators if the model has been misspecified. Our simulation results show that estimators based on the unit level model perform better than those based on the area level. The pseudo-EBLUP estimator is the best among unit level and area level estimators.

    Release date: 2016-06-22
  • 4. A note on regression estimation with unknown population size Archived
    Articles and reports: 12-001-X201600114543
    Description:

    The regression estimator is extensively used in practice because it can improve the reliability of the estimated parameters of interest such as means or totals. It uses control totals of variables known at the population level that are included in the regression set up. In this paper, we investigate the properties of the regression estimator that uses control totals estimated from the sample, as well as those known at the population level. This estimator is compared to the regression estimators that strictly use the known totals both theoretically and via a simulation study.

    Release date: 2016-06-22
  • 5. Model-based small area estimation under informative sampling Archived
    Articles and reports: 12-001-X201500214248
    Description:

    Unit level population models are often used in model-based small area estimation of totals and means, but the models may not hold for the sample if the sampling design is informative for the model. As a result, standard methods, assuming that the model holds for the sample, can lead to biased estimators. We study alternative methods that use a suitable function of the unit selection probability as an additional auxiliary variable in the sample model. We report the results of a simulation study on the bias and mean squared error (MSE) of the proposed estimators of small area means and on the relative bias of the associated MSE estimators, using informative sampling schemes to generate the samples. Alternative methods, based on modeling the conditional expectation of the design weight as a function of the model covariates and the response, are also included in the simulation study.

    Release date: 2015-12-17
  • 6. A weighted composite likelihood approach to inference for two-level models from survey data Archived
    Articles and reports: 12-001-X201300211887
    Description:

    Multi-level models are extensively used for analyzing survey data with the design hierarchy matching the model hierarchy. We propose a unified approach, based on a design-weighted log composite likelihood, for two-level models that leads to design-model consistent estimators of the model parameters even when the within cluster sample sizes are small provided the number of sample clusters is large. This method can handle both linear and generalized linear two-level models and it requires level 2 and level 1 inclusion probabilities and level 1 joint inclusion probabilities, where level 2 represents a cluster and level 1 an element within a cluster. Results of a simulation study demonstrating superior performance of the proposed method relative to existing methods under informative sampling are also reported.

    Release date: 2014-01-15
  • 7. On the performance of self benchmarked small area estimators under the Fay-Herriot area level model Archived
    Articles and reports: 12-001-X201300111830
    Description:

    We consider two different self-benchmarking methods for the estimation of small area means based on the Fay-Herriot (FH) area level model: the method of You and Rao (2002) applied to the FH model and the method of Wang, Fuller and Qu (2008) based on augmented models. We derive an estimator of the mean squared prediction error (MSPE) of the You-Rao (YR) estimator of a small area mean that, under the true model, is correct to second-order terms. We report the results of a simulation study on the relative bias of the MSPE estimator of the YR estimator and the MSPE estimator of the Wang, Fuller and Qu (WFQ) estimator obtained under an augmented model. We also study the MSPE and the estimators of MSPE for the YR and WFQ estimators obtained under a misspecified model.

    Release date: 2013-06-28
  • 8. On sample allocation for efficient domain estimation Archived
    Articles and reports: 12-001-X201200111682
    Description:

    Sample allocation issues are studied in the context of estimating sub-population (stratum or domain) means as well as the aggregate population mean under stratified simple random sampling. A non-linear programming method is used to obtain "optimal" sample allocation to strata that minimizes the total sample size subject to specified tolerances on the coefficient of variation of the estimators of strata means and the population mean. The resulting total sample size is then used to determine sample allocations for the methods of Costa, Satorra and Ventura (2004) based on compromise allocation and Longford (2006) based on specified "inferential priorities". In addition, we study sample allocation to strata when reliability requirements for domains, cutting across strata, are also specified. Performance of the three methods is studied using data from Statistics Canada's Monthly Retail Trade Survey (MRTS) of single establishments.

    Release date: 2012-06-27
  • 9. A short history of the regression estimator in survey sampling Archived
    Articles and reports: 11-536-X200900110815
    Description:

    Regression estimation has only been used extensively by statistical organizations in recent years. The sample mean and the classical ratio estimator are particular cases of this estimator. Both have a long history that can be traced back to the ancient Greeks or earlier. For instance, the ratio estimator was used by the Egyptians and Babylonians to compute the circumference of a circle given its radius, given a constant approximating the famous pi value. Multiple examples that use the ratio estimator as a means to indirectly compute a measure of interest can be found in physics, and engineering. The ratio estimator was also used to estimate population census estimates in the past when exact counts were beyond the capabilities of the existing administrations (for example John Graunt 1662, and Laplace late eighteenth century).

    In this talk, we will trace the evolution of the regression estimation in survey sampling from the 1930's to the present time. We will outline its advantages and disadvantages in using it in survey sampling. Corresponding software development will also be presented

    Release date: 2009-08-11
  • 10. Redesigning the UK's business register and business surveys to enable production of better economic statistics Archived
    Articles and reports: 11-522-X20050019453
    Description:

    The UK's Office for National Statistics (ONS) is starting a development programme for business surveys to meet the recommendations of a recent government report calling for improvements to economic statistics, in particular regional economic statistics.

    Release date: 2007-03-02
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Articles and reports (15)

Articles and reports (15) (0 to 10 of 15 results)

  • 1. Application of sampling variance smoothing methods for small area proportion estimation Archived
    Articles and reports: 11-522-X202200100005
    Description: Sampling variance smoothing is an important topic in small area estimation. In this paper, we propose sampling variance smoothing methods for small area proportion estimation. In particular, we consider the generalized variance function and design effect methods for sampling variance smoothing. We evaluate and compare the smoothed sampling variances and small area estimates based on the smoothed variance estimates through analysis of survey data from Statistics Canada. The results from real data analysis indicate that the proposed sampling variance smoothing methods work very well for small area estimation.
    Release date: 2024-03-25
  • 2. Non-response follow-up for business surveys
    Articles and reports: 12-001-X202200100006
    Description:

    In the last two decades, survey response rates have been steadily falling. In that context, it has become increasingly important for statistical agencies to develop and use methods that reduce the adverse effects of non-response on the accuracy of survey estimates. Follow-up of non-respondents may be an effective, albeit time and resource-intensive, remedy for non-response bias. We conducted a simulation study using real business survey data to shed some light on several questions about non-response follow-up. For instance, assuming a fixed non-response follow-up budget, what is the best way to select non-responding units to be followed up? How much effort should be dedicated to repeatedly following up non-respondents until a response is received? Should they all be followed up or a sample of them? If a sample is followed up, how should it be selected? We compared Monte Carlo relative biases and relative root mean square errors under different follow-up sampling designs, sample sizes and non-response scenarios. We also determined an expression for the minimum follow-up sample size required to expend the budget, on average, and showed that it maximizes the expected response rate. A main conclusion of our simulation experiment is that this sample size also appears to approximately minimize the bias and mean square error of the estimates.

    Release date: 2022-06-21
  • 3. Comparison of unit level and area level small area estimators Archived
    Articles and reports: 12-001-X201600114540
    Description:

    In this paper, we compare the EBLUP and pseudo-EBLUP estimators for small area estimation under the nested error regression model and three area level model-based estimators using the Fay-Herriot model. We conduct a design-based simulation study to compare the model-based estimators for unit level and area level models under informative and non-informative sampling. In particular, we are interested in the confidence interval coverage rate of the unit level and area level estimators. We also compare the estimators if the model has been misspecified. Our simulation results show that estimators based on the unit level model perform better than those based on the area level. The pseudo-EBLUP estimator is the best among unit level and area level estimators.

    Release date: 2016-06-22
  • 4. A note on regression estimation with unknown population size Archived
    Articles and reports: 12-001-X201600114543
    Description:

    The regression estimator is extensively used in practice because it can improve the reliability of the estimated parameters of interest such as means or totals. It uses control totals of variables known at the population level that are included in the regression set up. In this paper, we investigate the properties of the regression estimator that uses control totals estimated from the sample, as well as those known at the population level. This estimator is compared to the regression estimators that strictly use the known totals both theoretically and via a simulation study.

    Release date: 2016-06-22
  • 5. Model-based small area estimation under informative sampling Archived
    Articles and reports: 12-001-X201500214248
    Description:

    Unit level population models are often used in model-based small area estimation of totals and means, but the models may not hold for the sample if the sampling design is informative for the model. As a result, standard methods, assuming that the model holds for the sample, can lead to biased estimators. We study alternative methods that use a suitable function of the unit selection probability as an additional auxiliary variable in the sample model. We report the results of a simulation study on the bias and mean squared error (MSE) of the proposed estimators of small area means and on the relative bias of the associated MSE estimators, using informative sampling schemes to generate the samples. Alternative methods, based on modeling the conditional expectation of the design weight as a function of the model covariates and the response, are also included in the simulation study.

    Release date: 2015-12-17
  • 6. A weighted composite likelihood approach to inference for two-level models from survey data Archived
    Articles and reports: 12-001-X201300211887
    Description:

    Multi-level models are extensively used for analyzing survey data with the design hierarchy matching the model hierarchy. We propose a unified approach, based on a design-weighted log composite likelihood, for two-level models that leads to design-model consistent estimators of the model parameters even when the within cluster sample sizes are small provided the number of sample clusters is large. This method can handle both linear and generalized linear two-level models and it requires level 2 and level 1 inclusion probabilities and level 1 joint inclusion probabilities, where level 2 represents a cluster and level 1 an element within a cluster. Results of a simulation study demonstrating superior performance of the proposed method relative to existing methods under informative sampling are also reported.

    Release date: 2014-01-15
  • 7. On the performance of self benchmarked small area estimators under the Fay-Herriot area level model Archived
    Articles and reports: 12-001-X201300111830
    Description:

    We consider two different self-benchmarking methods for the estimation of small area means based on the Fay-Herriot (FH) area level model: the method of You and Rao (2002) applied to the FH model and the method of Wang, Fuller and Qu (2008) based on augmented models. We derive an estimator of the mean squared prediction error (MSPE) of the You-Rao (YR) estimator of a small area mean that, under the true model, is correct to second-order terms. We report the results of a simulation study on the relative bias of the MSPE estimator of the YR estimator and the MSPE estimator of the Wang, Fuller and Qu (WFQ) estimator obtained under an augmented model. We also study the MSPE and the estimators of MSPE for the YR and WFQ estimators obtained under a misspecified model.

    Release date: 2013-06-28
  • 8. On sample allocation for efficient domain estimation Archived
    Articles and reports: 12-001-X201200111682
    Description:

    Sample allocation issues are studied in the context of estimating sub-population (stratum or domain) means as well as the aggregate population mean under stratified simple random sampling. A non-linear programming method is used to obtain "optimal" sample allocation to strata that minimizes the total sample size subject to specified tolerances on the coefficient of variation of the estimators of strata means and the population mean. The resulting total sample size is then used to determine sample allocations for the methods of Costa, Satorra and Ventura (2004) based on compromise allocation and Longford (2006) based on specified "inferential priorities". In addition, we study sample allocation to strata when reliability requirements for domains, cutting across strata, are also specified. Performance of the three methods is studied using data from Statistics Canada's Monthly Retail Trade Survey (MRTS) of single establishments.

    Release date: 2012-06-27
  • 9. A short history of the regression estimator in survey sampling Archived
    Articles and reports: 11-536-X200900110815
    Description:

    Regression estimation has only been used extensively by statistical organizations in recent years. The sample mean and the classical ratio estimator are particular cases of this estimator. Both have a long history that can be traced back to the ancient Greeks or earlier. For instance, the ratio estimator was used by the Egyptians and Babylonians to compute the circumference of a circle given its radius, given a constant approximating the famous pi value. Multiple examples that use the ratio estimator as a means to indirectly compute a measure of interest can be found in physics, and engineering. The ratio estimator was also used to estimate population census estimates in the past when exact counts were beyond the capabilities of the existing administrations (for example John Graunt 1662, and Laplace late eighteenth century).

    In this talk, we will trace the evolution of the regression estimation in survey sampling from the 1930's to the present time. We will outline its advantages and disadvantages in using it in survey sampling. Corresponding software development will also be presented

    Release date: 2009-08-11
  • 10. Redesigning the UK's business register and business surveys to enable production of better economic statistics Archived
    Articles and reports: 11-522-X20050019453
    Description:

    The UK's Office for National Statistics (ONS) is starting a development programme for business surveys to meet the recommendations of a recent government report calling for improvements to economic statistics, in particular regional economic statistics.

    Release date: 2007-03-02
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