摘要翻译：
在一个两阶段的整群抽样过程中，从独立的群体中独立地抽取$n$随机群体，并在每个群体中抽取一个子样本。观测变量的一般均值估计是渐近高斯的，研究了归一化统计量和研究统计量的几种bootstrap形式的渐近分布。加权总体重采样提供了一个很好的近似，其精度依赖于总体样本量的收敛速度。
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英文标题：
《Bootstrap of means under stratified sampling》
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作者：
Odile Pons
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最新提交年份：
2007
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分类信息：

一级分类：Mathematics        数学
二级分类：Statistics Theory        统计理论
分类描述：Applied, computational and theoretical statistics: e.g. statistical inference, regression, time series, multivariate analysis, data analysis, Markov chain Monte Carlo, design of experiments, case studies
应用统计、计算统计和理论统计：例如统计推断、回归、时间序列、多元分析、数据分析、马尔可夫链蒙特卡罗、实验设计、案例研究
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一级分类：Statistics        统计学
二级分类：Statistics Theory        统计理论
分类描述：stat.TH is an alias for math.ST. Asymptotics, Bayesian Inference, Decision Theory, Estimation, Foundations, Inference, Testing.
Stat.Th是Math.St的别名。渐近，贝叶斯推论，决策理论，估计，基础，推论，检验。
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英文摘要：
  In a two-stage cluster sampling procedure, $n$ random populations are drawn independently from independent populations and a sub-sample of observations is taken in each of them. The estimator of the general mean of the observed variables is asymptotically Gaussian and the asymptotic distributions of several bootstrap versions of the normalized and studentized statistics are studied. A weighted population resampling provides a good approximation and its accuracy depends on the convergence rate of the sample size of the populations.
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PDF链接：
https://arxiv.org/pdf/709.3246