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Abstract Details
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Activity Number:
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218
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Type:
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Invited
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Date/Time:
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Monday, July 30, 2012 : 2:00 PM to 3:50 PM
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Sponsor:
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WNAR
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| Abstract - #303856 |
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Title:
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Using Nonparametric Regression to Derive Predictors in Analytic Inference from Complex Surveys
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Author(s):
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Jay Breidt*+ and Jean Opsomer and Wade Herndon
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Companies:
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Colorado State University and Colorado State University and Colorado State University
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Address:
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201 Statistics , Fort Collins, CO, 80523,
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Keywords:
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analytic inference ;
complex survey ;
nonparametric regression ;
informative selection
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Abstract:
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In analytic inference for a regression relationship in a complex survey, informative selection may lead to inconsistent estimation. A standard approach is to expand the covariates to account for design features, but these design covariates may be of no scientific interest. An approach to removing these design covariates using nonparametric regression is explored. The resulting predictor has variance that can be estimated using a combination of standard design-based variance estimation and model-based variance estimation.
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