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Evaluating the Effective Degrees of Freedom of the Delete-a-Group Jackknife

机译:评估“删除一组折刀”的有效自由度

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The delete-a-group jackknife is sometimes used when estimating the variances of statistics based on a large sample. We investigate heavily poststratified estimators for a population mean and a simple regression coefficient, where both full-sample and domain estimates are of interest. The delete-a-group (DAG) jackknife employing 30, 60, and 100 replicates is found to be highly unstable, even for large sample sizes. The empirical degrees of freedom of these DAG jackknives are usually much less than their nominal degrees of freedom. This analysis calls into question whether coverage intervals derived from replication-based variance estimators can be trusted for highly calibrated estimates.
机译:在基于大样本估计统计量方差时,有时会使用删除组折刀。我们调查了人口均值和简单回归系数的大量后分层估计量,其中全样本估计和域估计都令人感兴趣。发现即使使用大样本量,使用30、60和100个重复的删除组(DAG)折刀也是高度不稳定的。这些DAG切刀的经验自由度通常比其名义自由度小得多。这项分析引起了疑问,对于高度校准的估计值,是否可以信任从基于复制的方差估计值得出的覆盖间隔。

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