Keyword search
Results
All (5)
All (5) ((5 results))
- Articles and reports: 12-001-X201300111823Description:
Although weights are widely used in survey sampling their ultimate justification from the design perspective is often problematical. Here we will argue for a stepwise Bayes justification for weights that does not depend explicitly on the sampling design. This approach will make use of the standard kind of information present in auxiliary variables however it will not assume a model relating the auxiliary variables to the characteristic of interest. The resulting weight for a unit in the sample can be given the usual interpretation as the number of units in the population which it represents.
Release date: 2013-06-28 - Articles and reports: 12-001-X201300111826Description:
It is routine practice for survey organizations to provide replication weights as part of survey data files. These replication weights are meant to produce valid and efficient variance estimates for a variety of estimators in a simple and systematic manner. Most existing methods for constructing replication weights, however, are only valid for specific sampling designs and typically require a very large number of replicates. In this paper we first show how to produce replication weights based on the method outlined in Fay (1984) such that the resulting replication variance estimator is algebraically equivalent to the fully efficient linearization variance estimator for any given sampling design. We then propose a novel weight-calibration method to simultaneously achieve efficiency and sparsity in the sense that a small number of sets of replication weights can produce valid and efficient replication variance estimators for key population parameters. Our proposed method can be used in conjunction with existing resampling techniques for large-scale complex surveys. Validity of the proposed methods and extensions to some balanced sampling designs are also discussed. Simulation results showed that our proposed variance estimators perform very well in tracking coverage probabilities of confidence intervals. Our proposed strategies will likely have impact on how public-use survey data files are produced and how these data sets are analyzed.
Release date: 2013-06-28 - 3. Estimation of the variance of cross-sectional indicators for the SILC survey in Switzerland ArchivedArticles and reports: 12-001-X201300111827Description:
SILC (Statistics on Income and Living Conditions) is an annual European survey that measures the population's income distribution, poverty and living conditions. It has been conducted in Switzerland since 2007, based on a four-panel rotation scheme that yields both cross-sectional and longitudinal estimates. This article examines the problem of estimating the variance of the cross-sectional poverty and social exclusion indicators selected by Eurostat. Our calculations take into account the non-linearity of the estimators, total non-response at different survey stages, indirect sampling and calibration. We adapt the method proposed by Lavallée (2002) for estimating variance in cases of non-response after weight sharing, and we obtain a variance estimator that is asymptotically unbiased and very easy to program.
Release date: 2013-06-28 - 4. Indirect sampling applied to skewed populations ArchivedArticles and reports: 12-001-X201300111829Description:
Indirect Sampling is used when the sampling frame is not the same as the target population, but related to the latter. The estimation process for Indirect Sampling is carried out using the Generalised Weight Share Method (GWSM), which is an unbiased procedure (see Lavallée 2002, 2007). For business surveys, Indirect Sampling is applied as follows: the sampling frame is one of establishments, while the target population is one of enterprises. Enterprises are selected through their establishments. This allows stratifying according to the establishment characteristics, rather than those associated with enterprises. Because the variables of interest of establishments are generally highly skewed (a small portion of the establishments covers the major portion of the economy), the GWSM results in unbiased estimates, but their variance can be large. The purpose of this paper is to suggest some adjustments to the weights to reduce the variance of the estimates in the context of skewed populations, while keeping the method unbiased. After a brief overview of Indirect Sampling and the GWSM, we describe the required adjustments to the GWSM. The estimates produced with these adjustments are compared to those from the original GWSM, via a small numerical example, and using real data originating from the Statistics Canada's Business Register.
Release date: 2013-06-28 - 5. Conservative variance estimation for sampling designs with zero pairwise inclusion probabilities ArchivedArticles and reports: 12-001-X201300111831Description:
We consider conservative variance estimation for the Horvitz-Thompson estimator of a population total in sampling designs with zero pairwise inclusion probabilities, known as "non-measurable" designs. We decompose the standard Horvitz-Thompson variance estimator under such designs and characterize the bias precisely. We develop a bias correction that is guaranteed to be weakly conservative (nonnegatively biased) regardless of the nature of the non-measurability. The analysis sheds light on conditions under which the standard Horvitz-Thompson variance estimator performs well despite non-measurability and where the conservative bias correction may outperform commonly-used approximations.
Release date: 2013-06-28
Data (0)
Data (0) (0 results)
No content available at this time.
Analysis (5)
Analysis (5) ((5 results))
- Articles and reports: 12-001-X201300111823Description:
Although weights are widely used in survey sampling their ultimate justification from the design perspective is often problematical. Here we will argue for a stepwise Bayes justification for weights that does not depend explicitly on the sampling design. This approach will make use of the standard kind of information present in auxiliary variables however it will not assume a model relating the auxiliary variables to the characteristic of interest. The resulting weight for a unit in the sample can be given the usual interpretation as the number of units in the population which it represents.
Release date: 2013-06-28 - Articles and reports: 12-001-X201300111826Description:
It is routine practice for survey organizations to provide replication weights as part of survey data files. These replication weights are meant to produce valid and efficient variance estimates for a variety of estimators in a simple and systematic manner. Most existing methods for constructing replication weights, however, are only valid for specific sampling designs and typically require a very large number of replicates. In this paper we first show how to produce replication weights based on the method outlined in Fay (1984) such that the resulting replication variance estimator is algebraically equivalent to the fully efficient linearization variance estimator for any given sampling design. We then propose a novel weight-calibration method to simultaneously achieve efficiency and sparsity in the sense that a small number of sets of replication weights can produce valid and efficient replication variance estimators for key population parameters. Our proposed method can be used in conjunction with existing resampling techniques for large-scale complex surveys. Validity of the proposed methods and extensions to some balanced sampling designs are also discussed. Simulation results showed that our proposed variance estimators perform very well in tracking coverage probabilities of confidence intervals. Our proposed strategies will likely have impact on how public-use survey data files are produced and how these data sets are analyzed.
Release date: 2013-06-28 - 3. Estimation of the variance of cross-sectional indicators for the SILC survey in Switzerland ArchivedArticles and reports: 12-001-X201300111827Description:
SILC (Statistics on Income and Living Conditions) is an annual European survey that measures the population's income distribution, poverty and living conditions. It has been conducted in Switzerland since 2007, based on a four-panel rotation scheme that yields both cross-sectional and longitudinal estimates. This article examines the problem of estimating the variance of the cross-sectional poverty and social exclusion indicators selected by Eurostat. Our calculations take into account the non-linearity of the estimators, total non-response at different survey stages, indirect sampling and calibration. We adapt the method proposed by Lavallée (2002) for estimating variance in cases of non-response after weight sharing, and we obtain a variance estimator that is asymptotically unbiased and very easy to program.
Release date: 2013-06-28 - 4. Indirect sampling applied to skewed populations ArchivedArticles and reports: 12-001-X201300111829Description:
Indirect Sampling is used when the sampling frame is not the same as the target population, but related to the latter. The estimation process for Indirect Sampling is carried out using the Generalised Weight Share Method (GWSM), which is an unbiased procedure (see Lavallée 2002, 2007). For business surveys, Indirect Sampling is applied as follows: the sampling frame is one of establishments, while the target population is one of enterprises. Enterprises are selected through their establishments. This allows stratifying according to the establishment characteristics, rather than those associated with enterprises. Because the variables of interest of establishments are generally highly skewed (a small portion of the establishments covers the major portion of the economy), the GWSM results in unbiased estimates, but their variance can be large. The purpose of this paper is to suggest some adjustments to the weights to reduce the variance of the estimates in the context of skewed populations, while keeping the method unbiased. After a brief overview of Indirect Sampling and the GWSM, we describe the required adjustments to the GWSM. The estimates produced with these adjustments are compared to those from the original GWSM, via a small numerical example, and using real data originating from the Statistics Canada's Business Register.
Release date: 2013-06-28 - 5. Conservative variance estimation for sampling designs with zero pairwise inclusion probabilities ArchivedArticles and reports: 12-001-X201300111831Description:
We consider conservative variance estimation for the Horvitz-Thompson estimator of a population total in sampling designs with zero pairwise inclusion probabilities, known as "non-measurable" designs. We decompose the standard Horvitz-Thompson variance estimator under such designs and characterize the bias precisely. We develop a bias correction that is guaranteed to be weakly conservative (nonnegatively biased) regardless of the nature of the non-measurability. The analysis sheds light on conditions under which the standard Horvitz-Thompson variance estimator performs well despite non-measurability and where the conservative bias correction may outperform commonly-used approximations.
Release date: 2013-06-28
Reference (0)
Reference (0) (0 results)
No content available at this time.
- Date modified: