摘要翻译：
本文提出了一种分层抽样算法，该算法利用在地层中随机绘制的图来计算兴趣期望，并自适应地修改在每个地层中进一步绘制的图的比例。这些比例在方差减少方面收敛到最优分配。并且我们的分层估计是渐近正态的，且渐近方差等于最小方差。数值实验验证了算法的有效性。
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英文标题：
《Adaptive optimal allocation in stratified sampling methods》
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作者：
Pierre Etore (CERMICS), Benjamin Jourdain (CERMICS)
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最新提交年份：
2007
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分类信息：

一级分类：Statistics        统计学
二级分类：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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一级分类：Statistics        统计学
二级分类：Computation        计算
分类描述：Algorithms, Simulation, Visualization
算法、模拟、可视化
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英文摘要：
  In this paper, we propose a stratified sampling algorithm in which the random drawings made in the strata to compute the expectation of interest are also used to adaptively modify the proportion of further drawings in each stratum. These proportions converge to the optimal allocation in terms of variance reduction. And our stratified estimator is asymptotically normal with asymptotic variance equal to the minimal one. Numerical experiments confirm the efficiency of our algorithm.
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PDF链接：
https://arxiv.org/pdf/711.4514