Multi-step variance minimization in sequential tests

Zheng Su; Jiaqiao Hu; Wei Zhu

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Multi-step variance minimization in sequential tests

机译：顺序测试中的多步方差最小化

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摘要

We introduce a multi-step variance minimization algorithm for numerical estimation of Type Ⅰ and Type Ⅱ error probabilities in sequential tests. The algorithm can be applied to general test statistics and easily built into general design algorithms for sequential tests. Our simulation results indicate that the proposed algorithm is particularly useful for estimating tail probabilities, and may lead to significant computational efficiency gains over the crude Monte Carlo method.

机译：针对顺序测试中Ⅰ类和Ⅱ类错误概率的数值估计，我们引入了一种多步方差最小化算法。该算法可以应用于常规测试统计数据，并且可以轻松地内置到用于顺序测试的常规设计算法中。我们的仿真结果表明，所提出的算法对于估计尾部概率特别有用，并且可能导致比原始的蒙特卡洛方法显着的计算效率提高。

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来源
《Statistics and computing》 |2008年第1期|p.101-108|共8页
作者
Zheng Su; Jiaqiao Hu; Wei Zhu;
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作者单位

Genentech Inc, South San Francisco, CA 94080, USA;

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原文格式 PDF
正文语种 eng
中图分类统计学;
关键词
type Ⅰ error; type Ⅱ error; importance sampling; monte carlo;

机译：Ⅰ型误差;Ⅱ型误差;重要抽样;蒙特卡洛;

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Multi-step variance minimization in sequential tests

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