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• 1. Using the t-distribution to deal with outliers in small area estimation Archived

  Articles and reports: 11-522-X200600110391

  Description:

  Small area estimation using linear area level models typically assumes normality of the area level random effects (model errors) and of the survey errors of the direct survey estimates. Outlying observations can be a concern, and can arise from outliers in either the model errors or the survey errors, two possibilities with very different implications. We consider both possibilities here and investigate empirically how use of a Bayesian approach with a t-distribution assumed for one of the error components can address potential outliers. The empirical examples use models for U.S. state poverty ratios from the U.S. Census Bureau's Small Area Income and Poverty Estimates program, extending the usual Gaussian models to assume a t-distribution for the model error or survey error. Results are examined to see how they are affected by varying the number of degrees of freedom (assumed known) of the t-distribution. We find that using a t-distribution with low degrees of freedom can diminish the effects of outliers, but in the examples discussed the results do not go as far as approaching outright rejection of observations.

  Release date: 2008-03-17

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• 2. Hierarchical and empirical Bayes methods for adjustment of census undercount: The 1988 Missouri dress rehearsal data Archived

  Articles and reports: 12-001-X199200114497

  Description:

  The present article discusses a model-based approach towards adjustment of the 1988 Census Dress Rehearsal Data collected from test sites in Missouri. The primary objective is to develop procedures that can be used to model data from the 1990 Census Post Enumeration Survey in April, 1991 and smooth survey-based estimates of the adjustment factors. We have proposed in this paper hierarchical Bayes (HB) and empirical Bayes (EB) procedures which meet this objective. The resulting estimators seem to improve consistently on the estimators of the adjustment factors based on dual system estimation (DSE) as well as the smoothed regression estimators.

  Release date: 1992-06-15

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Articles and reports (2) ((2 results))

• 1. Using the t-distribution to deal with outliers in small area estimation Archived

  Articles and reports: 11-522-X200600110391

  Description:

  Small area estimation using linear area level models typically assumes normality of the area level random effects (model errors) and of the survey errors of the direct survey estimates. Outlying observations can be a concern, and can arise from outliers in either the model errors or the survey errors, two possibilities with very different implications. We consider both possibilities here and investigate empirically how use of a Bayesian approach with a t-distribution assumed for one of the error components can address potential outliers. The empirical examples use models for U.S. state poverty ratios from the U.S. Census Bureau's Small Area Income and Poverty Estimates program, extending the usual Gaussian models to assume a t-distribution for the model error or survey error. Results are examined to see how they are affected by varying the number of degrees of freedom (assumed known) of the t-distribution. We find that using a t-distribution with low degrees of freedom can diminish the effects of outliers, but in the examples discussed the results do not go as far as approaching outright rejection of observations.

  Release date: 2008-03-17

  ---

• 2. Hierarchical and empirical Bayes methods for adjustment of census undercount: The 1988 Missouri dress rehearsal data Archived

  Articles and reports: 12-001-X199200114497

  Description:

  The present article discusses a model-based approach towards adjustment of the 1988 Census Dress Rehearsal Data collected from test sites in Missouri. The primary objective is to develop procedures that can be used to model data from the 1990 Census Post Enumeration Survey in April, 1991 and smooth survey-based estimates of the adjustment factors. We have proposed in this paper hierarchical Bayes (HB) and empirical Bayes (EB) procedures which meet this objective. The resulting estimators seem to improve consistently on the estimators of the adjustment factors based on dual system estimation (DSE) as well as the smoothed regression estimators.

  Release date: 1992-06-15

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