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Activity Number:
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510
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Type:
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Contributed
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Date/Time:
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Thursday, August 7, 2008 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Survey Research Methods
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| Abstract - #300763 |
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Title:
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Inference in Sampling Problems Using Regression Models Imposed by Randomization in the Sample Design - Called Pre-Sampling
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Author(s):
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Steve Woodruff*+
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Companies:
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Specified Designs
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Address:
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800 West View Terrace, Alexandria, VA, 22301-2750,
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Keywords:
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Probability Sampling ; Regression Models ; Combined Ratio Estimator ; Best Linear Unbiased Estimator
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Abstract:
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The variance of a probability expansion estimator is sensitive to sample design and can be large when the design is subject to administrative and physical constraints. Models and model based estimates provide a more efficient alternative but are dependent on models of questionable validity and sacrifice the impartiality of randomization. There is a third estimation technique that retains the comforting impartiality of randomization and uses this randomization to impose a model on the sample data under which there is a Best Linear Unbiased Estimator (BLUE). Since the model is imposed by the statistician through designed randomization, model failure tends toward a non-issue. Examples from actual surveys are provided where the sampling variance of the Combined Ratio Estimator is tens to hundreds of times greater than that of the BLUE.
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