Delete-m Jackknife for Unequal m

In this paper, the delete-mj jackknife estimator is proposed. This estimator is based on samples obtained from the original sample by successively removing mutually exclusive groups of unequal size. In a Monte Carlo simulation study, a hierarchical linear model was used to evaluate the role of nonnormal residuals and sample size on bias and eciency of this estimator. It is shown that bias is reduced in exchange for a minor reduction in eciency. The accompanying jackknife variance estimator even improves on both bias and eciency, and, moreover, this estimator is mean-squared-error consistent, whereas the maximum likelihood equivalents are not.