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Abstract
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

Abstract:
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.

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

Copyright Statistics Sweden 1996-2018.  Open Access
ISSN 0282-423X
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