Monte Carlo Approximation of Bootstrap Variances
Monte Carlo Approximation of Bootstrap Variances
复制标题
Bootstrap 方差的蒙特卡罗逼近
DOI：
10.1080/00031305.1998.10480596
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发表时间：
1998
期刊：
影响因子：
--
通讯作者：
S. Sarkar
中科院分区：
文献类型：
--
作者：
J. Booth;S. Sarkar
人们普遍认为，自举方差估计所需的重样本数量相对较少。基于蒙特卡洛近似的无条件变异系数的论点表明，只要25个重样本就能给出合理的结果。在本文中，我们认为，重采样的数量实际上应该由条件变异系数决定，只涉及重采样变异性。我们的条件分析建立在不允许蒙特卡罗误差决定统计分析结论的信念之上，并表明为此目的需要大约800个样本。该论点可以推广到多元设置，并给出了一个简单的公式，用于确定近似m维自举方差-协方差矩阵所需的样本数的下界。
Abstract It is widely believed that the number of resamples required for bootstrap variance estimation is relatively small An argument based on the unconditional coefficient of variation of the Monte Carlo approximation, suggests that as few as 25 resamples will give reasonable results. In this article we argue that the number of resamples should, in fact, be determined by the conditional coefficient of variation, involving only resampling variability. Our conditional analysis is founded on a belief that Monte Carlo error should not be allowed to determine the conclusions of a statistical analysis and indicates that approximately 800 resamples are required for this purpose. The argument can be generalized to the multivariate setting and a simple formula is given for determining a lower bound on the number of resamples required to approximate an m-dimensional bootstrap variance-covariance matrix.