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• 1. Maximum likelihood estimation for contingency tables and logistic regression with incorrectly linked data Archived

  Articles and reports: 12-001-X201100111444

  Description:

  Data linkage is the act of bringing together records that are believed to belong to the same unit (e.g., person or business) from two or more files. It is a very common way to enhance dimensions such as time and breadth or depth of detail. Data linkage is often not an error-free process and can lead to linking a pair of records that do not belong to the same unit. There is an explosion of record linkage applications, yet there has been little work on assuring the quality of analyses using such linked files. Naively treating such a linked file as if it were linked without errors will, in general, lead to biased estimates. This paper develops a maximum likelihood estimator for contingency tables and logistic regression with incorrectly linked records. The estimation technique is simple and is implemented using the well-known EM algorithm. A well known method of linking records in the present context is probabilistic data linking. The paper demonstrates the effectiveness of the proposed estimators in an empirical study which uses probabilistic data linkage.

  Release date: 2011-06-29

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• 2. Efficient bootstrap for business surveys Archived

  Articles and reports: 12-001-X200700210494

  Description:

  The Australian Bureau of Statistics has recently developed a generalized estimation system for processing its large scale annual and sub-annual business surveys. Designs for these surveys have a large number of strata, use Simple Random Sampling within Strata, have non-negligible sampling fractions, are overlapping in consecutive periods, and are subject to frame changes. A significant challenge was to choose a variance estimation method that would best meet the following requirements: valid for a wide range of estimators (e.g., ratio and generalized regression), requires limited computation time, can be easily adapted to different designs and estimators, and has good theoretical properties measured in terms of bias and variance. This paper describes the Without Replacement Scaled Bootstrap (WOSB) that was implemented at the ABS and shows that it is appreciably more efficient than the Rao and Wu (1988)'s With Replacement Scaled Bootstrap (WSB). The main advantages of the Bootstrap over alternative replicate variance estimators are its efficiency (i.e., accuracy per unit of storage space) and the relative simplicity with which it can be specified in a system. This paper describes the WOSB variance estimator for point-in-time and movement estimates that can be expressed as a function of finite population means. Simulation results obtained as part of the evaluation process show that the WOSB was more efficient than the WSB, especially when the stratum sample sizes are sometimes as small as 5.

  Release date: 2008-01-03

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Articles and reports (2) ((2 results))

• 1. Maximum likelihood estimation for contingency tables and logistic regression with incorrectly linked data Archived

  Articles and reports: 12-001-X201100111444

  Description:

  Data linkage is the act of bringing together records that are believed to belong to the same unit (e.g., person or business) from two or more files. It is a very common way to enhance dimensions such as time and breadth or depth of detail. Data linkage is often not an error-free process and can lead to linking a pair of records that do not belong to the same unit. There is an explosion of record linkage applications, yet there has been little work on assuring the quality of analyses using such linked files. Naively treating such a linked file as if it were linked without errors will, in general, lead to biased estimates. This paper develops a maximum likelihood estimator for contingency tables and logistic regression with incorrectly linked records. The estimation technique is simple and is implemented using the well-known EM algorithm. A well known method of linking records in the present context is probabilistic data linking. The paper demonstrates the effectiveness of the proposed estimators in an empirical study which uses probabilistic data linkage.

  Release date: 2011-06-29

  ---

• 2. Efficient bootstrap for business surveys Archived

  Articles and reports: 12-001-X200700210494

  Description:

  The Australian Bureau of Statistics has recently developed a generalized estimation system for processing its large scale annual and sub-annual business surveys. Designs for these surveys have a large number of strata, use Simple Random Sampling within Strata, have non-negligible sampling fractions, are overlapping in consecutive periods, and are subject to frame changes. A significant challenge was to choose a variance estimation method that would best meet the following requirements: valid for a wide range of estimators (e.g., ratio and generalized regression), requires limited computation time, can be easily adapted to different designs and estimators, and has good theoretical properties measured in terms of bias and variance. This paper describes the Without Replacement Scaled Bootstrap (WOSB) that was implemented at the ABS and shows that it is appreciably more efficient than the Rao and Wu (1988)'s With Replacement Scaled Bootstrap (WSB). The main advantages of the Bootstrap over alternative replicate variance estimators are its efficiency (i.e., accuracy per unit of storage space) and the relative simplicity with which it can be specified in a system. This paper describes the WOSB variance estimator for point-in-time and movement estimates that can be expressed as a function of finite population means. Simulation results obtained as part of the evaluation process show that the WOSB was more efficient than the WSB, especially when the stratum sample sizes are sometimes as small as 5.

  Release date: 2008-01-03

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