Editing and imputation

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  • 1. Imputing distributions in administrative tax data Archived
    Articles and reports: 11-522-X20050019458
    Description:

    The proposed paper presents an alternative methodology that gives the data the possibility of defining homogenous groups determined by a bottom up classification of the values of observed details. The problem is then to assign a non respondent business to one of these groups. Several assignment procedures, based on explanatory variables available in the tax returns, are compared, using gross or distributed data: parametric and non parametric classification analyses, log linear models, etc.

    Release date: 2007-03-02
  • 2. Methods for longitudinal binary data with missing observations Archived
    Articles and reports: 11-522-X20050019474
    Description:

    Missingness is a common feature of longitudinal studies. In recent years there has been considerable research devoted to the development of methods for the analysis of incomplete longitudinal data. One common practice is imputation by the " last observation carried forward" (LOCF) approach, in which values for missing responses are imputed using observations from the most recently completed assessment. In this talk I will first examine the performance of the LOCF approach where the generalized estimating equations (GEE) are employed as the inferential procedures.

    Release date: 2007-03-02
  • 3. Quality indicators when combining survey data and administrative data Archived
    Articles and reports: 11-522-X20050019494
    Description:

    Traditionally, data quality indicators reported by surveys have been the sampling variance, coverage error, non-response rate and imputation rate. To obtain an imputation rate when combining survey data and administrative data, one of the problems is to compute the imputation rate itself. The presentation will discuss how to solve this problem. First, we will discuss the desired properties when developing a rate in a general context. Second, we will develop some concepts and definitions that will help us to develop combine rates. Third, we will propose different combined rates for the case of imputation. We will then present three different combined rates, and we will discuss properties for each rate. We will end with some illustrative examples.

    Release date: 2007-03-02
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  • 1. Imputing distributions in administrative tax data Archived
    Articles and reports: 11-522-X20050019458
    Description:

    The proposed paper presents an alternative methodology that gives the data the possibility of defining homogenous groups determined by a bottom up classification of the values of observed details. The problem is then to assign a non respondent business to one of these groups. Several assignment procedures, based on explanatory variables available in the tax returns, are compared, using gross or distributed data: parametric and non parametric classification analyses, log linear models, etc.

    Release date: 2007-03-02
  • 2. Methods for longitudinal binary data with missing observations Archived
    Articles and reports: 11-522-X20050019474
    Description:

    Missingness is a common feature of longitudinal studies. In recent years there has been considerable research devoted to the development of methods for the analysis of incomplete longitudinal data. One common practice is imputation by the " last observation carried forward" (LOCF) approach, in which values for missing responses are imputed using observations from the most recently completed assessment. In this talk I will first examine the performance of the LOCF approach where the generalized estimating equations (GEE) are employed as the inferential procedures.

    Release date: 2007-03-02
  • 3. Quality indicators when combining survey data and administrative data Archived
    Articles and reports: 11-522-X20050019494
    Description:

    Traditionally, data quality indicators reported by surveys have been the sampling variance, coverage error, non-response rate and imputation rate. To obtain an imputation rate when combining survey data and administrative data, one of the problems is to compute the imputation rate itself. The presentation will discuss how to solve this problem. First, we will discuss the desired properties when developing a rate in a general context. Second, we will develop some concepts and definitions that will help us to develop combine rates. Third, we will propose different combined rates for the case of imputation. We will then present three different combined rates, and we will discuss properties for each rate. We will end with some illustrative examples.

    Release date: 2007-03-02
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