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  • Articles and reports: 12-001-X201900100002
    Description:

    Item nonresponse is frequently encountered in sample surveys. Hot-deck imputation is commonly used to fill in missing item values within homogeneous groups called imputation classes. We propose a fractional hot-deck imputation procedure and an associated empirical likelihood for inference on the population mean of a function of a variable of interest with missing data under probability proportional to size sampling with negligible sampling fractions. We derive the limiting distributions of the maximum empirical likelihood estimator and empirical likelihood ratio, and propose two related asymptotically valid bootstrap procedures to construct confidence intervals for the population mean. Simulation studies show that the proposed bootstrap procedures outperform the customary bootstrap procedures which are shown to be asymptotically incorrect when the number of random draws in the fractional imputation is fixed. Moreover, the proposed bootstrap procedure based on the empirical likelihood ratio is seen to perform significantly better than the method based on the limiting distribution of the maximum empirical likelihood estimator when the inclusion probabilities vary considerably or when the sample size is not large.

    Release date: 2019-05-07

  • Surveys and statistical programs – Documentation: 62F0026M2011001
    Description:

    This report describes the quality indicators produced for the 2009 Survey of Household Spending. These quality indicators, such as coefficients of variation, nonresponse rates, slippage rates and imputation rates, help users interpret the survey data.

    Release date: 2011-06-16

  • Articles and reports: 62F0026M2005003
    Description:

    The Food Expenditure Survey (FES) is a periodic survey collecting data from households on food spending habits. Data are collected mainly using weekly diaries of purchases that the respondents must fill in daily during two consecutive weeks.

    The FES, like all surveys, is subject to error despite all the precautions taken at the various stages of the survey to control them. Although there is no exhaustive measure of a survey's data quality, certain quality measures taken at various stages of the survey can provide the user with relevant information to ensure sound data interpretation.

    This paper presents, for the 2001 FES, the following quality indicators the coefficients of variation, the non-response rates, the vacancy rates, the slippage rates, the imputation rates as well the impacts of imputation on the estimates.

    Release date: 2005-07-08

  • Articles and reports: 11-522-X20030017708
    Description:

    This article provides an overview of the work to date using GST data at Statistics Canada as direct replacement in imputation or estimation or as a data certification tool.

    Release date: 2005-01-26

  • Articles and reports: 11-522-X20030017712
    Description:

    This paper discusse variance estimation in the presence of imputations with an application to price index estimation, multiphase sampling, and the use of graphics in publications.

    Release date: 2005-01-26

  • Surveys and statistical programs – Documentation: 62F0026M2004001
    Description:

    This report describes the quality indicators produced for the 2002 Survey of Household Spending. These quality indicators, such as coefficients of variation, nonresponse rates, slippage rates and imputation rates, help users interpret the survey data.

    Release date: 2004-09-15

  • Surveys and statistical programs – Documentation: 92-391-X
    Description:

    This report contains basic conceptual and data quality information intended to facilitate the use and interpretation of census industry data. It provides an overview of the industry processing cycle, including elements such as regional processing, edit and imputation, and the tabulation of error rates. A detailed explanation of the automated coding systems used in the 2001 Census is also documented, in addition to notable changes in the imputation procedures. The report concludes with summary tables that indicate the level of data quality in the 2001 Census industry data. Appendices to the report contain historical data going back to the 1971 Census.

    Release date: 2004-06-02

  • Articles and reports: 12-001-X199200214483
    Description:

    In almost all large surveys, some form of imputation is used. This paper develops a method for variance estimation when single (as opposed to multiple) imputation is used to create a completed data set. Imputation will never reproduce the true values (except in truly exceptional cases). The total error of the survey estimate is viewed in this paper as the sum of sampling error and imputation error. Consequently, an overall variance is derived as the sum of a sampling variance and an imputation variance. The principal theme is the estimation of these two components, using the data after imputation, that is, the actually observed values and the imputed values. The approach is model assisted in the sense that the model implied by the imputation method and the randomization distribution used for sample selection will together determine the appearance of the variance estimators. The theoretical findings are confirmed by a Monte Carlo simulation.

    Release date: 1992-12-15

  • Articles and reports: 12-001-X198800114598
    Description:

    This paper discusses methods used to handle missing data in post-enumeration surveys for estimating census coverage error, as illustrated for the 1986 Test of Adjustment Related Operations (Diffendal 1988). The methods include imputation schemes based on hot-deck and logistic regression models as well as weighting adjustments. The sensitivity of undercount estimates from the 1986 test to variations in the imputation models is also explored.

    Release date: 1988-06-15

  • Articles and reports: 12-001-X198600214449
    Description:

    Nearly all surveys and censuses are subject to two types of nonresponse: unit (total) and item (partial). Several methods of compensating for nonresponse have been developed in an attempt to reduce the bias associated with nonresponse. This paper summarizes the nonresponse adjustment procedures used at the U.S. Census Bureau, focusing on unit nonresponse. Some discussion of current and future research in this area is also included.

    Release date: 1986-12-15
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  • Articles and reports: 12-001-X201900100002
    Description:

    Item nonresponse is frequently encountered in sample surveys. Hot-deck imputation is commonly used to fill in missing item values within homogeneous groups called imputation classes. We propose a fractional hot-deck imputation procedure and an associated empirical likelihood for inference on the population mean of a function of a variable of interest with missing data under probability proportional to size sampling with negligible sampling fractions. We derive the limiting distributions of the maximum empirical likelihood estimator and empirical likelihood ratio, and propose two related asymptotically valid bootstrap procedures to construct confidence intervals for the population mean. Simulation studies show that the proposed bootstrap procedures outperform the customary bootstrap procedures which are shown to be asymptotically incorrect when the number of random draws in the fractional imputation is fixed. Moreover, the proposed bootstrap procedure based on the empirical likelihood ratio is seen to perform significantly better than the method based on the limiting distribution of the maximum empirical likelihood estimator when the inclusion probabilities vary considerably or when the sample size is not large.

    Release date: 2019-05-07

  • Articles and reports: 62F0026M2005003
    Description:

    The Food Expenditure Survey (FES) is a periodic survey collecting data from households on food spending habits. Data are collected mainly using weekly diaries of purchases that the respondents must fill in daily during two consecutive weeks.

    The FES, like all surveys, is subject to error despite all the precautions taken at the various stages of the survey to control them. Although there is no exhaustive measure of a survey's data quality, certain quality measures taken at various stages of the survey can provide the user with relevant information to ensure sound data interpretation.

    This paper presents, for the 2001 FES, the following quality indicators the coefficients of variation, the non-response rates, the vacancy rates, the slippage rates, the imputation rates as well the impacts of imputation on the estimates.

    Release date: 2005-07-08

  • Articles and reports: 11-522-X20030017708
    Description:

    This article provides an overview of the work to date using GST data at Statistics Canada as direct replacement in imputation or estimation or as a data certification tool.

    Release date: 2005-01-26

  • Articles and reports: 11-522-X20030017712
    Description:

    This paper discusse variance estimation in the presence of imputations with an application to price index estimation, multiphase sampling, and the use of graphics in publications.

    Release date: 2005-01-26

  • Articles and reports: 12-001-X199200214483
    Description:

    In almost all large surveys, some form of imputation is used. This paper develops a method for variance estimation when single (as opposed to multiple) imputation is used to create a completed data set. Imputation will never reproduce the true values (except in truly exceptional cases). The total error of the survey estimate is viewed in this paper as the sum of sampling error and imputation error. Consequently, an overall variance is derived as the sum of a sampling variance and an imputation variance. The principal theme is the estimation of these two components, using the data after imputation, that is, the actually observed values and the imputed values. The approach is model assisted in the sense that the model implied by the imputation method and the randomization distribution used for sample selection will together determine the appearance of the variance estimators. The theoretical findings are confirmed by a Monte Carlo simulation.

    Release date: 1992-12-15

  • Articles and reports: 12-001-X198800114598
    Description:

    This paper discusses methods used to handle missing data in post-enumeration surveys for estimating census coverage error, as illustrated for the 1986 Test of Adjustment Related Operations (Diffendal 1988). The methods include imputation schemes based on hot-deck and logistic regression models as well as weighting adjustments. The sensitivity of undercount estimates from the 1986 test to variations in the imputation models is also explored.

    Release date: 1988-06-15

  • Articles and reports: 12-001-X198600214449
    Description:

    Nearly all surveys and censuses are subject to two types of nonresponse: unit (total) and item (partial). Several methods of compensating for nonresponse have been developed in an attempt to reduce the bias associated with nonresponse. This paper summarizes the nonresponse adjustment procedures used at the U.S. Census Bureau, focusing on unit nonresponse. Some discussion of current and future research in this area is also included.

    Release date: 1986-12-15

  • Articles and reports: 12-001-X198600114404
    Description:

    Missing survey data occur because of total nonresponse and item nonresponse. The standard way to attempt to compensate for total nonresponse is by some form of weighting adjustment, whereas item nonresponses are handled by some form of imputation. This paper reviews methods of weighting adjustment and imputation and discusses their properties.

    Release date: 1986-06-16

  • Articles and reports: 12-001-X198600114441
    Description:

    The analysis of survey data becomes difficult in the presence of incomplete responses. By the use of the maximum likelihood method, estimators for the parameters of interest and test statistics can be generated. In this paper the maximum likelihood estimators are given for the case where the data is considered missing at random. A method for imputing the missing values is considered along with the problem of estimating the change points in the mean. Possible extensions of the results to structured covariances and to non-randomly incomplete data are also proposed.

    Release date: 1986-06-16

  • Articles and reports: 12-001-X198600114442
    Description:

    For periodic business surveys which are conducted on a monthly, quarterly or annual basis, the data for responding units must be edited and the data for non-responding units must be imputed. This paper reports on methods which can be used for editing and imputing data. The editing is comprised of consistency and statistical edits. The imputation is done for both total non-response and partial non-response.

    Release date: 1986-06-16
Reference (3)

Reference (3) ((3 results))

  • Surveys and statistical programs – Documentation: 62F0026M2011001
    Description:

    This report describes the quality indicators produced for the 2009 Survey of Household Spending. These quality indicators, such as coefficients of variation, nonresponse rates, slippage rates and imputation rates, help users interpret the survey data.

    Release date: 2011-06-16

  • Surveys and statistical programs – Documentation: 62F0026M2004001
    Description:

    This report describes the quality indicators produced for the 2002 Survey of Household Spending. These quality indicators, such as coefficients of variation, nonresponse rates, slippage rates and imputation rates, help users interpret the survey data.

    Release date: 2004-09-15

  • Surveys and statistical programs – Documentation: 92-391-X
    Description:

    This report contains basic conceptual and data quality information intended to facilitate the use and interpretation of census industry data. It provides an overview of the industry processing cycle, including elements such as regional processing, edit and imputation, and the tabulation of error rates. A detailed explanation of the automated coding systems used in the 2001 Census is also documented, in addition to notable changes in the imputation procedures. The report concludes with summary tables that indicate the level of data quality in the 2001 Census industry data. Appendices to the report contain historical data going back to the 1971 Census.

    Release date: 2004-06-02
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