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  • 1. Hot deck imputation procedure applied to a double sampling design Archived
    Articles and reports: 12-001-X198600214450
    Description:

    From an annual sample of U.S. corporate tax returns, the U.S. Internal Revenue Service provides estimates of population and subpopulation totals for several hundred financial items. The basic sample design is highly stratified and fairly complex. Starting with the 1981 and 1982 samples, the design was altered to include a double sampling procedure. This was motivated by the need for better allocation of resources, in an environment of shrinking budgets. Items not observed in the subsample are predicted, using a modified hot deck imputation procedure. The present paper describes the design, estimation, and evaluation of the effects of the new procedure.

    Release date: 1986-12-15
  • 2. A regression approach to estimation in the presence of nonresponse Archived
    Articles and reports: 12-001-X198600214462
    Description:

    In the presence of unit nonresponse, two types of variables can sometimes be observed for units in the “intended” sample s, namely, (a) variables used to estimate the response mechanism (the response probabilities), (b) variables (here called co-variates) that explain the variable of interest, in the usual regression theory sense. This paper, based on Särndal and Swensson (1985 a, b), discusses nonresponse adjusted estimators with and without explicit involvement of co-variates. We conclude that the presence of strong co-variates in an estimator induces several favourable properties. Among other things, estimators making use of co-variates are considerably more resistant to nonresponse bias. We discuss the calculation of standard error and valid confidence intervals for estimators involving co-variates. The structure of the standard error is examined and discussed.

    Release date: 1986-12-15
  • 3. The maximum likelihood method for non-response in sample surveys Archived
    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
  • 4. A study of the effects of imputation groups in the nearest neighbour imputation method for the National Farm Survey Archived
    Articles and reports: 12-001-X198600114444
    Description:

    A new processing system using the nearest neighbour (N-N) imputation method is being implemented for the National Farm Survey (NFS). An empirical study was conducted to determine if the NFS estimates would be affected by using imputation groups based on type of farm. For the specific imputation rule examined, the study showed evidence that the effect might be small.

    Release date: 1986-06-16
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Articles and reports (4)

Articles and reports (4) ((4 results))

  • 1. Hot deck imputation procedure applied to a double sampling design Archived
    Articles and reports: 12-001-X198600214450
    Description:

    From an annual sample of U.S. corporate tax returns, the U.S. Internal Revenue Service provides estimates of population and subpopulation totals for several hundred financial items. The basic sample design is highly stratified and fairly complex. Starting with the 1981 and 1982 samples, the design was altered to include a double sampling procedure. This was motivated by the need for better allocation of resources, in an environment of shrinking budgets. Items not observed in the subsample are predicted, using a modified hot deck imputation procedure. The present paper describes the design, estimation, and evaluation of the effects of the new procedure.

    Release date: 1986-12-15
  • 2. A regression approach to estimation in the presence of nonresponse Archived
    Articles and reports: 12-001-X198600214462
    Description:

    In the presence of unit nonresponse, two types of variables can sometimes be observed for units in the “intended” sample s, namely, (a) variables used to estimate the response mechanism (the response probabilities), (b) variables (here called co-variates) that explain the variable of interest, in the usual regression theory sense. This paper, based on Särndal and Swensson (1985 a, b), discusses nonresponse adjusted estimators with and without explicit involvement of co-variates. We conclude that the presence of strong co-variates in an estimator induces several favourable properties. Among other things, estimators making use of co-variates are considerably more resistant to nonresponse bias. We discuss the calculation of standard error and valid confidence intervals for estimators involving co-variates. The structure of the standard error is examined and discussed.

    Release date: 1986-12-15
  • 3. The maximum likelihood method for non-response in sample surveys Archived
    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
  • 4. A study of the effects of imputation groups in the nearest neighbour imputation method for the National Farm Survey Archived
    Articles and reports: 12-001-X198600114444
    Description:

    A new processing system using the nearest neighbour (N-N) imputation method is being implemented for the National Farm Survey (NFS). An empirical study was conducted to determine if the NFS estimates would be affected by using imputation groups based on type of farm. For the specific imputation rule examined, the study showed evidence that the effect might be small.

    Release date: 1986-06-16
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