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All (3) ((3 results))

  • 1. Conditional calibration and the sage statistician Archived
    Articles and reports: 12-001-X201900200010
    Description:

    Being a calibrated statistician means using procedures that in long-run practice basically follow the guidelines of Neyman’s approach to frequentist inference, which dominates current statistical thinking. Being a sage (i.e., wise) statistician when confronted with a particular data set means employing some Bayesian and Fiducial modes of thinking to moderate simple Neymanian calibration, even if not doing so formally. This article explicates this marriage of ideas using the concept of conditional calibration, which takes advantage of more recent simulation-based ideas arising in Approximate Bayesian Computation.

    Release date: 2019-06-27
  • 2. Imputation strategies for missing values in post-enumeration surveys Archived
    Articles and reports: 12-001-X198800214591
    Description:

    To estimate census undercount, a post-enumeration survey (PES) is taken, and an attempt is made to find a matching census record for each individual in the PES; the rate of successful matching provides an estimate of census coverage. Undercount estimation is performed within poststrata defined by geographic, demographic, and housing characteristics, X. Portions of X are missing for some individuals due to survey nonresponse; moreover, a match status Y cannot be determined for all individuals. A procedure is needed for imputing the missing values of X and Y. This paper reviews the imputation methods used in the 1986 Test of Adjustment Related Operations (Schenker 1988) and proposes two alternative model-based methods: (1) a maximum-likelihood contingency-table estimation procedure that ignores the missing-data mechanism; and (2) a new Bayesian contingency table estimation procedure that does not ignore the missing-data mechanism. The first method is computationally simpler, but the second is preferred on conceptual and scientific grounds.

    Release date: 1988-12-15
  • 3. Basic ideas of multiple imputation for nonresponse Archived
    Articles and reports: 12-001-X198600114439
    Description:

    Multiple imputation is a technique for handling survey nonresponse that replaces each missing value created by nonresponse by a vector of possible values that reflect uncertainty about which values to impute. A simple example and brief overview of the underlying theory are used to introduce the general procedure.

    Release date: 1986-06-16
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Articles and reports (3)

Articles and reports (3) ((3 results))

  • 1. Conditional calibration and the sage statistician Archived
    Articles and reports: 12-001-X201900200010
    Description:

    Being a calibrated statistician means using procedures that in long-run practice basically follow the guidelines of Neyman’s approach to frequentist inference, which dominates current statistical thinking. Being a sage (i.e., wise) statistician when confronted with a particular data set means employing some Bayesian and Fiducial modes of thinking to moderate simple Neymanian calibration, even if not doing so formally. This article explicates this marriage of ideas using the concept of conditional calibration, which takes advantage of more recent simulation-based ideas arising in Approximate Bayesian Computation.

    Release date: 2019-06-27
  • 2. Imputation strategies for missing values in post-enumeration surveys Archived
    Articles and reports: 12-001-X198800214591
    Description:

    To estimate census undercount, a post-enumeration survey (PES) is taken, and an attempt is made to find a matching census record for each individual in the PES; the rate of successful matching provides an estimate of census coverage. Undercount estimation is performed within poststrata defined by geographic, demographic, and housing characteristics, X. Portions of X are missing for some individuals due to survey nonresponse; moreover, a match status Y cannot be determined for all individuals. A procedure is needed for imputing the missing values of X and Y. This paper reviews the imputation methods used in the 1986 Test of Adjustment Related Operations (Schenker 1988) and proposes two alternative model-based methods: (1) a maximum-likelihood contingency-table estimation procedure that ignores the missing-data mechanism; and (2) a new Bayesian contingency table estimation procedure that does not ignore the missing-data mechanism. The first method is computationally simpler, but the second is preferred on conceptual and scientific grounds.

    Release date: 1988-12-15
  • 3. Basic ideas of multiple imputation for nonresponse Archived
    Articles and reports: 12-001-X198600114439
    Description:

    Multiple imputation is a technique for handling survey nonresponse that replaces each missing value created by nonresponse by a vector of possible values that reflect uncertainty about which values to impute. A simple example and brief overview of the underlying theory are used to introduce the general procedure.

    Release date: 1986-06-16
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