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- Articles and reports: 18-001-X2016001Description:
Although the record linkage of business data is not a completely new topic, the fact remains that the public and many data users are unaware of the programs and practices commonly used by statistical agencies across the world.
This report is a brief overview of the main practices, programs and challenges of record linkage of statistical agencies across the world who answered a short survey on this subject supplemented by publically available documentation produced by these agencies. The document shows that the linkage practices are similar between these statistical agencies; however the main differences are in the procedures in place to access to data along with regulatory policies that govern the record linkage permissions and the dissemination of data.
Release date: 2016-10-27 - 2. Statistical matching using fractional imputation ArchivedArticles and reports: 12-001-X201600114539Description:
Statistical matching is a technique for integrating two or more data sets when information available for matching records for individual participants across data sets is incomplete. Statistical matching can be viewed as a missing data problem where a researcher wants to perform a joint analysis of variables that are never jointly observed. A conditional independence assumption is often used to create imputed data for statistical matching. We consider a general approach to statistical matching using parametric fractional imputation of Kim (2011) to create imputed data under the assumption that the specified model is fully identified. The proposed method does not have a convergent EM sequence if the model is not identified. We also present variance estimators appropriate for the imputation procedure. We explain how the method applies directly to the analysis of data from split questionnaire designs and measurement error models.
Release date: 2016-06-22
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- Articles and reports: 18-001-X2016001Description:
Although the record linkage of business data is not a completely new topic, the fact remains that the public and many data users are unaware of the programs and practices commonly used by statistical agencies across the world.
This report is a brief overview of the main practices, programs and challenges of record linkage of statistical agencies across the world who answered a short survey on this subject supplemented by publically available documentation produced by these agencies. The document shows that the linkage practices are similar between these statistical agencies; however the main differences are in the procedures in place to access to data along with regulatory policies that govern the record linkage permissions and the dissemination of data.
Release date: 2016-10-27 - 2. Statistical matching using fractional imputation ArchivedArticles and reports: 12-001-X201600114539Description:
Statistical matching is a technique for integrating two or more data sets when information available for matching records for individual participants across data sets is incomplete. Statistical matching can be viewed as a missing data problem where a researcher wants to perform a joint analysis of variables that are never jointly observed. A conditional independence assumption is often used to create imputed data for statistical matching. We consider a general approach to statistical matching using parametric fractional imputation of Kim (2011) to create imputed data under the assumption that the specified model is fully identified. The proposed method does not have a convergent EM sequence if the model is not identified. We also present variance estimators appropriate for the imputation procedure. We explain how the method applies directly to the analysis of data from split questionnaire designs and measurement error models.
Release date: 2016-06-22
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