Weighting and estimation
Results
All (5)
All (5) ((5 results))
- Articles and reports: 12-001-X199300214455Description:
Post-stratification is a common technique for improving precision of estimators by using data items not available at the design stage of a survey. In large, complex samples, the vector of Horvitz-Thompson estimators of survey target variables and of post-stratum population sizes will, under appropriate conditions, be approximately multivariate normal. This large sample normality leads to a new post-stratified regression estimator, which is analogous to the linear regression estimator in simple random sampling. We derive the large sample design bias and mean squared errors of this new estimator, the standard post-stratified estimator, the Horvitz-Thompson estimator, and a ratio estimator. We use both real and artificial populations to study empirically the conditional and unconditional properties of the estimators in multistage sampling.
Release date: 1993-12-15 - Articles and reports: 12-001-X199300214458Description:
In this article we report the results of fitting a state-space model to Canadian unemployment rates. The model assumes an additive decomposition of the population values into a trend, seasonal and irregular component and separate autoregressive relationships for the six survey error series corresponding to the six monthly panel estimators. The model includes rotation group effects and permits the design variances of the survey errors to change over time. The model is fitted at the small area level but it accounts for correlations between the component series of different areas. The robustness of estimators obtained under the model is achieved by imposing the constraint that the monthly aggregate model based estimators in a group of small areas for which the total sample size is sufficiently large coincide with the corresponding direct survey estimators. The performance of the model when fitted to the Atlantic provinces is assessed by a variety of diagnostic statistics and residual plots and by comparisons with estimators in current use.
Release date: 1993-12-15 - Articles and reports: 12-001-X199300214459Description:
Record linkage is the matching of records containing data on individuals, businesses or dwellings when a unique identifier is not available. Methods used in practice involve classification of record pairs as links and non-links using an automated procedure based on the theoretical framework introduced by Fellegi and Sunter (1969). The estimation of classification error rates is an important issue. Fellegi and Sunter provide a method for calculation of classification error rate estimates as a direct by-product of linkage. These model-based estimates are easier to produce than the estimates based on manual matching of samples that are typically used in practice. Properties of model-based classification error rate estimates obtained using three estimators of model parameters are compared.
Release date: 1993-12-15 - Articles and reports: 12-001-X199300214460Description:
Methods for estimating response bias in surveys require “unbiased” remeasurements for at least a subsample of observations. The usual estimator of response bias is the difference between the mean of the original observations and the mean of the unbiased observations. In this article, we explore a number of alternative estimators of response bias derived from a model prediction approach. The assumed sampling design is a stratified two-phase design implementing simple random sampling in each phase. We assume that the characteristic, y, is observed for each unit selected in phase 1 while the true value of the characteristic, \mu, is obtained for each unit in the subsample selected at phase 2. We further assume that an auxiliary variable x is known for each unit in the phase 1 sample and that the population total of x is known. A number of models relating y, \mu and x are assumed which yield alternative estimators of E (y - \mu), the response bias. The estimators are evaluated using a bootstrap procedure for estimating variance, bias, and mean squared error. Our bootstrap procedure is an extension of the Bickel-Freedman single phase method to the case of a stratified two-phase design. As an illustration, the methodology is applied to data from the National Agricultural Statistics Service reinterview program. For these data, we show that the usual difference estimator is outperformed by the model-assisted estimator suggested by Särndal, Swensson and Wretman (1991), thus indicating that improvements over the traditional estimator are possible using the model prediction approach.
Release date: 1993-12-15 - Articles and reports: 12-001-X199300114471Description:
Binomial-Poisson and Poisson-Poisson sampling are introduced for use in forest sampling. Several estimators of the population total are discussed for these designs. Simulation comparisons of the properties of the estimators were made for three small forestry populations. A modification of the standard estimator used for Poisson sampling and a new estimator, called a modified Srivastava estimator, appear to be most efficient. The latter is unfortunately badly biased for all 3 populations.
Release date: 1993-06-15
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Analysis (5)
Analysis (5) ((5 results))
- Articles and reports: 12-001-X199300214455Description:
Post-stratification is a common technique for improving precision of estimators by using data items not available at the design stage of a survey. In large, complex samples, the vector of Horvitz-Thompson estimators of survey target variables and of post-stratum population sizes will, under appropriate conditions, be approximately multivariate normal. This large sample normality leads to a new post-stratified regression estimator, which is analogous to the linear regression estimator in simple random sampling. We derive the large sample design bias and mean squared errors of this new estimator, the standard post-stratified estimator, the Horvitz-Thompson estimator, and a ratio estimator. We use both real and artificial populations to study empirically the conditional and unconditional properties of the estimators in multistage sampling.
Release date: 1993-12-15 - Articles and reports: 12-001-X199300214458Description:
In this article we report the results of fitting a state-space model to Canadian unemployment rates. The model assumes an additive decomposition of the population values into a trend, seasonal and irregular component and separate autoregressive relationships for the six survey error series corresponding to the six monthly panel estimators. The model includes rotation group effects and permits the design variances of the survey errors to change over time. The model is fitted at the small area level but it accounts for correlations between the component series of different areas. The robustness of estimators obtained under the model is achieved by imposing the constraint that the monthly aggregate model based estimators in a group of small areas for which the total sample size is sufficiently large coincide with the corresponding direct survey estimators. The performance of the model when fitted to the Atlantic provinces is assessed by a variety of diagnostic statistics and residual plots and by comparisons with estimators in current use.
Release date: 1993-12-15 - Articles and reports: 12-001-X199300214459Description:
Record linkage is the matching of records containing data on individuals, businesses or dwellings when a unique identifier is not available. Methods used in practice involve classification of record pairs as links and non-links using an automated procedure based on the theoretical framework introduced by Fellegi and Sunter (1969). The estimation of classification error rates is an important issue. Fellegi and Sunter provide a method for calculation of classification error rate estimates as a direct by-product of linkage. These model-based estimates are easier to produce than the estimates based on manual matching of samples that are typically used in practice. Properties of model-based classification error rate estimates obtained using three estimators of model parameters are compared.
Release date: 1993-12-15 - Articles and reports: 12-001-X199300214460Description:
Methods for estimating response bias in surveys require “unbiased” remeasurements for at least a subsample of observations. The usual estimator of response bias is the difference between the mean of the original observations and the mean of the unbiased observations. In this article, we explore a number of alternative estimators of response bias derived from a model prediction approach. The assumed sampling design is a stratified two-phase design implementing simple random sampling in each phase. We assume that the characteristic, y, is observed for each unit selected in phase 1 while the true value of the characteristic, \mu, is obtained for each unit in the subsample selected at phase 2. We further assume that an auxiliary variable x is known for each unit in the phase 1 sample and that the population total of x is known. A number of models relating y, \mu and x are assumed which yield alternative estimators of E (y - \mu), the response bias. The estimators are evaluated using a bootstrap procedure for estimating variance, bias, and mean squared error. Our bootstrap procedure is an extension of the Bickel-Freedman single phase method to the case of a stratified two-phase design. As an illustration, the methodology is applied to data from the National Agricultural Statistics Service reinterview program. For these data, we show that the usual difference estimator is outperformed by the model-assisted estimator suggested by Särndal, Swensson and Wretman (1991), thus indicating that improvements over the traditional estimator are possible using the model prediction approach.
Release date: 1993-12-15 - Articles and reports: 12-001-X199300114471Description:
Binomial-Poisson and Poisson-Poisson sampling are introduced for use in forest sampling. Several estimators of the population total are discussed for these designs. Simulation comparisons of the properties of the estimators were made for three small forestry populations. A modification of the standard estimator used for Poisson sampling and a new estimator, called a modified Srivastava estimator, appear to be most efficient. The latter is unfortunately badly biased for all 3 populations.
Release date: 1993-06-15
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