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Abstract Details
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Activity Number:
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669
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Type:
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Contributed
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Date/Time:
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Thursday, August 2, 2012 : 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 - #304510 |
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Title:
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Properties of Smoothed Design-Based Variance Estimators from Complex Sample Surveys
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Author(s):
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MoonJung Cho*+ and John Eltinge and Julie Gershunskaya and Larry Huff
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Companies:
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Bureau of Labor Statistics and Bureau of Labor Statistics and Bureau of Labor Statistics and Bureau of Labor Statistics
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Address:
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9676 Scotch Haven Drive, Vienna, VA, 22181, United States
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Keywords:
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Bias ;
Design-based inference ;
Model-based inference ;
Superpopulation model ;
U.S. Current Employment Statistics Program ;
Variance estimator stability
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Abstract:
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Due to relatively high levels of sampling variability, direct design-based variance estimators are often smoothed before publication. For example, some Federal Statistics programs publish the medians of a sequence of monthly direct variance estimates, or functions of these medians. The properties of these smoothed estimators depend on several underlying conditions, including sample size, effective degrees of freedom for the direct estimators; correlation of the direct estimators across months; and temporal patterns in the true variances. We compare and contrast these properties with the corresponding properties of generalized variance function (GVF) estimators.
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