Editing and imputation
Results
All (3)
All (3) ((3 results))
- Articles and reports: 12-001-X201500114193Description:
Imputed micro data often contain conflicting information. The situation may e.g., arise from partial imputation, where one part of the imputed record consists of the observed values of the original record and the other the imputed values. Edit-rules that involve variables from both parts of the record will often be violated. Or, inconsistency may be caused by adjustment for errors in the observed data, also referred to as imputation in Editing. Under the assumption that the remaining inconsistency is not due to systematic errors, we propose to make adjustments to the micro data such that all constraints are simultaneously satisfied and the adjustments are minimal according to a chosen distance metric. Different approaches to the distance metric are considered, as well as several extensions of the basic situation, including the treatment of categorical data, unit imputation and macro-level benchmarking. The properties and interpretations of the proposed methods are illustrated using business-economic data.
Release date: 2015-06-29 - 2. Automatic Coding of Occupations ArchivedArticles and reports: 11-522-X201300014291Description:
Occupational coding in Germany is mostly done using dictionary approaches with subsequent manual revision of cases which could not be coded. Since manual coding is expensive, it is desirable to assign a higher number of codes automatically. At the same time the quality of the automatic coding must at least reach that of the manual coding. As a possible solution we employ different machine learning algorithms for the task using a substantial amount of manually coded occuptions available from recent studies as training data. We asses the feasibility of these methods of evaluating performance and quality of the algorithms.
Release date: 2014-10-31 - Articles and reports: 12-001-X20050018088Description:
When administrative records are geographically linked to census block groups, local-area characteristics from the census can be used as contextual variables, which may be useful supplements to variables that are not directly observable from the administrative records. Often databases contain records that have insufficient address information to permit geographical links with census block groups; the contextual variables for these records are therefore unobserved. We propose a new method that uses information from "matched cases" and multivariate regression models to create multiple imputations for the unobserved variables. Our method outperformed alternative methods in simulation evaluations using census data, and was applied to the dataset for a study on treatment patterns for colorectal cancer patients.
Release date: 2005-07-21
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Analysis (3)
Analysis (3) ((3 results))
- Articles and reports: 12-001-X201500114193Description:
Imputed micro data often contain conflicting information. The situation may e.g., arise from partial imputation, where one part of the imputed record consists of the observed values of the original record and the other the imputed values. Edit-rules that involve variables from both parts of the record will often be violated. Or, inconsistency may be caused by adjustment for errors in the observed data, also referred to as imputation in Editing. Under the assumption that the remaining inconsistency is not due to systematic errors, we propose to make adjustments to the micro data such that all constraints are simultaneously satisfied and the adjustments are minimal according to a chosen distance metric. Different approaches to the distance metric are considered, as well as several extensions of the basic situation, including the treatment of categorical data, unit imputation and macro-level benchmarking. The properties and interpretations of the proposed methods are illustrated using business-economic data.
Release date: 2015-06-29 - 2. Automatic Coding of Occupations ArchivedArticles and reports: 11-522-X201300014291Description:
Occupational coding in Germany is mostly done using dictionary approaches with subsequent manual revision of cases which could not be coded. Since manual coding is expensive, it is desirable to assign a higher number of codes automatically. At the same time the quality of the automatic coding must at least reach that of the manual coding. As a possible solution we employ different machine learning algorithms for the task using a substantial amount of manually coded occuptions available from recent studies as training data. We asses the feasibility of these methods of evaluating performance and quality of the algorithms.
Release date: 2014-10-31 - Articles and reports: 12-001-X20050018088Description:
When administrative records are geographically linked to census block groups, local-area characteristics from the census can be used as contextual variables, which may be useful supplements to variables that are not directly observable from the administrative records. Often databases contain records that have insufficient address information to permit geographical links with census block groups; the contextual variables for these records are therefore unobserved. We propose a new method that uses information from "matched cases" and multivariate regression models to create multiple imputations for the unobserved variables. Our method outperformed alternative methods in simulation evaluations using census data, and was applied to the dataset for a study on treatment patterns for colorectal cancer patients.
Release date: 2005-07-21
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