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All (3) ((3 results))

  • 1. Using calibration weighting to adjust for nonresponse and coverage errors Archived
    Articles and reports: 12-001-X20060029547
    Description:

    Calibration weighting can be used to adjust for unit nonresponse and/or coverage errors under appropriate quasi-randomization models. Alternative calibration adjustments that are asymptotically identical in a purely sampling context can diverge when used in this manner. Introducing instrumental variables into calibration weighting makes it possible for nonresponse (say) to be a function of a set of characteristics other than those in the calibration vector. When the calibration adjustment has a nonlinear form, a variant of the jackknife can remove the need for iteration in variance estimation.

    Release date: 2006-12-21
  • 2. On calibration estimation for quantiles Archived
    Articles and reports: 12-001-X20060019255
    Description:

    In this paper, we consider the estimation of quantiles using the calibration paradigm. The proposed methodology relies on an approach similar to the one leading to the original calibration estimators of Deville and Särndal (1992). An appealing property of the new methodology is that it is not necessary to know the values of the auxiliary variables for all units in the population. It suffices instead to know the corresponding quantiles for the auxiliary variables. When the quadratic metric is adopted, an analytic representation of the calibration weights is obtained. In this situation, the weights are similar to those leading to the generalized regression (GREG) estimator. Variance estimation and construction of confidence intervals are discussed. In a small simulation study, a calibration estimator is compared to other popular estimators for quantiles that also make use of auxiliary information.

    Release date: 2006-07-20
  • 3. The 2006 reverse record check sample allocation Archived
    Articles and reports: 12-001-X20060019261
    Description:

    Sample allocation can be optimized with respect to various goals. When there is more than one goal, a compromise allocation must be chosen. In the past, the Reverse Record Check achieved that compromise by having a certain fraction of the sample optimally allocated for each goal (for example, two thirds of the sample is allocated to produce good-quality provincial estimates, and one third to produce a good-quality national estimate). This paper suggests a method that involves selecting the maximum of two or more optimal allocations. By analyzing the impact that the precision of population estimates has on the federal government's equalization payments to the provinces, we can set four goals for the Reverse Record Check's provincial sample allocation. The Reverse Record Check's subprovincial sample allocation requires the smoothing of stratum-level parameters. This paper shows how calibration can be used to achieve this smoothing. The calibration problem and its solution do not assume that the calibration constraints have a solution. This avoids convergence problems inherent in related methods such as the raking ratio.

    Release date: 2006-07-20
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  • 1. Using calibration weighting to adjust for nonresponse and coverage errors Archived
    Articles and reports: 12-001-X20060029547
    Description:

    Calibration weighting can be used to adjust for unit nonresponse and/or coverage errors under appropriate quasi-randomization models. Alternative calibration adjustments that are asymptotically identical in a purely sampling context can diverge when used in this manner. Introducing instrumental variables into calibration weighting makes it possible for nonresponse (say) to be a function of a set of characteristics other than those in the calibration vector. When the calibration adjustment has a nonlinear form, a variant of the jackknife can remove the need for iteration in variance estimation.

    Release date: 2006-12-21
  • 2. On calibration estimation for quantiles Archived
    Articles and reports: 12-001-X20060019255
    Description:

    In this paper, we consider the estimation of quantiles using the calibration paradigm. The proposed methodology relies on an approach similar to the one leading to the original calibration estimators of Deville and Särndal (1992). An appealing property of the new methodology is that it is not necessary to know the values of the auxiliary variables for all units in the population. It suffices instead to know the corresponding quantiles for the auxiliary variables. When the quadratic metric is adopted, an analytic representation of the calibration weights is obtained. In this situation, the weights are similar to those leading to the generalized regression (GREG) estimator. Variance estimation and construction of confidence intervals are discussed. In a small simulation study, a calibration estimator is compared to other popular estimators for quantiles that also make use of auxiliary information.

    Release date: 2006-07-20
  • 3. The 2006 reverse record check sample allocation Archived
    Articles and reports: 12-001-X20060019261
    Description:

    Sample allocation can be optimized with respect to various goals. When there is more than one goal, a compromise allocation must be chosen. In the past, the Reverse Record Check achieved that compromise by having a certain fraction of the sample optimally allocated for each goal (for example, two thirds of the sample is allocated to produce good-quality provincial estimates, and one third to produce a good-quality national estimate). This paper suggests a method that involves selecting the maximum of two or more optimal allocations. By analyzing the impact that the precision of population estimates has on the federal government's equalization payments to the provinces, we can set four goals for the Reverse Record Check's provincial sample allocation. The Reverse Record Check's subprovincial sample allocation requires the smoothing of stratum-level parameters. This paper shows how calibration can be used to achieve this smoothing. The calibration problem and its solution do not assume that the calibration constraints have a solution. This avoids convergence problems inherent in related methods such as the raking ratio.

    Release date: 2006-07-20
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