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Rigorous error control methods for estimating means of bounded random variables

机译:估计有界随机变量均值的严格错误控制方法

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In this article, we propose rigorous sample size methods for estimating the means of random variables, which require no information of the underlying distributions except that the random variables are known to be bounded in a certain interval. Our sample size methods can be applied without assuming that the samples are identical and independent. Moreover, our sample size methods involve no approximation. We demonstrate that the sample complexity can be significantly reduced by using a mixed error criterion. We derive explicit sample size formulae to ensure the statistical accuracy of estimation. (c) 2014 Elsevier B.V. All rights reserved.
机译:在本文中,我们提出了严格的样本量方法来估计随机变量的均值,该方法不需要底层分布的信息,只是已知随机变量的边界是一定的范围。我们的样本量方法可以在不假设样本相同且独立的情况下应用。此外,我们的样本量方法不包含任何近似值。我们证明了使用混合误差准则可以大大降低样本复杂度。我们得出明确的样本量公式,以确保估计的统计准确性。 (c)2014 Elsevier B.V.保留所有权利。

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