Journal of Official Statistics, Vol.27, No.1, 2011. pp. 121134

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Evaluating the Small-Sample Bias 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 a delete-a-group jackknife employing either 15 or 30 replicates are evaluated and compared with the behavior of linearization variance estimators.

Calibrated weight, domain, ignorable sample design, linearization variance estimator, model parameter, relative empirical bias

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ISSN 0282-423X
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