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Bootstrap模拟的平衡设计AI 解读

SUMMARY Davison et al. (1986) have shown that finite bootstrap simulations can be improved by forcing balance in the aggregate of simulated data sets. Their methods yield first-order balance, which principally affects bootstrap estimation of bias. Here we extend the methodology to second-order balance, which principally affects bootstrap estimation of variance. The particular techniques involve Latin square and balanced incomplete block designs. Numerical examples are given…

重要性抽样与嵌套 bootstrapAI 解读

The nested bootstrap is a computer-intensive technique for reducing the statistical error in the ordinary bootstrap. We show how the Monte Carlo technique of importance sampling can be used to substantially reduce the amount of computation needed in a simple double bootstrap confidence limit method.

重要性抽样与嵌套自助法AI 解读

The nested bootstrap is a computer-intensive technique for reducing the statistical error in the ordinary bootstrap. We show how the Monte Carlo technique of importance sampling can be used to substantially reduce the amount of computation needed in a simple double bootstrap confidence limit method.