Multi-step variance minimization in sequential tests

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 indic...

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Bibliographic Details
Published inStatistics and computing Vol. 18; no. 1; pp. 101 - 108
Main Authors Su, Zheng, Hu, Jiaqiao, Zhu, Wei
Format Journal Article
LanguageEnglish
Published Boston Springer US 01.03.2008
Subjects
Artificial Intelligence
Mathematics and Statistics
Probability and Statistics in Computer Science
Statistical Theory and Methods
Statistics
Statistics and Computing/Statistics Programs
Type II error
Type I error
Monte Carlo
Importance sampling
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ISSN0960-3174
1573-1375
DOI10.1007/s11222-007-9041-z

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Summary: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.
ISSN:0960-3174
1573-1375
DOI:10.1007/s11222-007-9041-z