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Activity Number:
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33
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Type:
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Contributed
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Date/Time:
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Sunday, August 2, 2009 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Survey Research Methods
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| Abstract - #303379 |
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Title:
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Evaluating the Asymptotic Limits of the Delete-a-Group Jackknife for Model Analyses
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Author(s):
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Phillip S. Kott and Steven T. Garren*+
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Companies:
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James Madison University and U.S. Department of Agriculture
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Address:
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MSC 1911, Harrisonburg, VA, 22807,
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Keywords:
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Calibrated weight ; Domain ; Ignorable ; Linearization variance estimator ; Model parameter ; Relative empirical bias
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Abstract:
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The delete-a-group jackknife can be effectively used when estimating the variances of statistics based on a large sample. The theory supporting its use is asymptotic, however. Consequently, analysts have questioned its effectiveness when estimating parameters for a small domain computed using only a fraction of the large sample at hand. We investigate this issue empirically by focusing on heavily post-stratified estimators for a population mean and a simple regression coefficient, where the post-stratification takes place at the full-sample level. Samples are chosen using differentially-weighted Poisson sampling. The bias and stability of delete-a-group jackknife employing either 15 of 30 replicates are evaluated and compared with the behavior of linearization variance estimators.
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