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• 1. Calibration and restricted weights Archived

  Articles and reports: 12-001-X20000015182

  Description:

  To better understand the impact of imposing a restricted region on calibration weights, the author reviews the latter's aymptotic behaviour. Necessary and sufficient conditions are provided for the existence of a solution to the calibration equation with weights within given intervals.

  Release date: 2000-08-30

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• 2. Simultaneous calibration of several surveys Archived

  Surveys and statistical programs – Documentation: 11-522-X19990015684

  Description:

  Often, the same information is gathered almost simultaneously for several different surveys. In France, this practice is institutionalized for household surveys that have a common set of demographic variables, i.e., employment, residence and income. These variables are important co-factors for the variables of interest in each survey, and if used carefully, can reinforce the estimates derived from each survey. Techniques for calibrating uncertain data can apply naturally in this context. This involves finding the best unbiased estimator in common variables and calibrating each survey based on that estimator. The estimator thus obtained in each survey is always a linear estimator, the weightings of which can be easily explained and the variance can be obtained with no new problems, as can the variance estimate. To supplement the list of regression estimators, this technique can also be seen as a ridge-regression estimator, or as a Bayesian-regression estimator.

  Release date: 2000-03-02

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• 3. Cosmetic calibration with unequal probability sampling Archived

  Articles and reports: 12-001-X19990024883

  Description:

  Brewer proposes a method of weight calibration in survey sampling, called cosmetic calibration, which yields cosmetic estimators of totals, i.e. estimators that can be interpreted as both design-based and prediction based. He also discusses variance estimation and shows how the problem of negative weights can be easily and naturally handled using cosmetic calibration. Finally he compares the properties of the weights and the resulting estimators to some alternative approaches using some Australian far data.

  Release date: 2000-03-01

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• 1. Calibration and restricted weights Archived

  Articles and reports: 12-001-X20000015182

  Description:

  To better understand the impact of imposing a restricted region on calibration weights, the author reviews the latter's aymptotic behaviour. Necessary and sufficient conditions are provided for the existence of a solution to the calibration equation with weights within given intervals.

  Release date: 2000-08-30

  ---

• 2. Cosmetic calibration with unequal probability sampling Archived

  Articles and reports: 12-001-X19990024883

  Description:

  Brewer proposes a method of weight calibration in survey sampling, called cosmetic calibration, which yields cosmetic estimators of totals, i.e. estimators that can be interpreted as both design-based and prediction based. He also discusses variance estimation and shows how the problem of negative weights can be easily and naturally handled using cosmetic calibration. Finally he compares the properties of the weights and the resulting estimators to some alternative approaches using some Australian far data.

  Release date: 2000-03-01

Reference (1) ((1 result))

• 1. Simultaneous calibration of several surveys Archived

  Surveys and statistical programs – Documentation: 11-522-X19990015684

  Description:

  Often, the same information is gathered almost simultaneously for several different surveys. In France, this practice is institutionalized for household surveys that have a common set of demographic variables, i.e., employment, residence and income. These variables are important co-factors for the variables of interest in each survey, and if used carefully, can reinforce the estimates derived from each survey. Techniques for calibrating uncertain data can apply naturally in this context. This involves finding the best unbiased estimator in common variables and calibrating each survey based on that estimator. The estimator thus obtained in each survey is always a linear estimator, the weightings of which can be easily explained and the variance can be obtained with no new problems, as can the variance estimate. To supplement the list of regression estimators, this technique can also be seen as a ridge-regression estimator, or as a Bayesian-regression estimator.

  Release date: 2000-03-02