Evaluating the Asymptotic Limits of the Delete-a-Group Jackknife for Model Analyses

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 poststratified estimators for a population mean and a simple regression coefficient, where the poststratification 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 or 30 replicates are evaluated and compared with the behavior of linearization variance estimators.


Issue Date:
2009-05
Publication Type:
Report
PURL Identifier:
http://purl.umn.edu/234370
Total Pages:
18




 Record created 2017-04-01, last modified 2017-08-29

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