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首页> 外文期刊>Advances in Atmospheric Sciences >An Intercomparison of Rules for Testing the Significance of Coupled Modes of Singular Value Decomposition Analysis
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An Intercomparison of Rules for Testing the Significance of Coupled Modes of Singular Value Decomposition Analysis

机译：检验奇异值分解分析耦合模式重要性的规则的比较

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摘要

This paper clarifies the essence of the significance test of singular value decomposition analysis (SVD), and investigates four rules for testing the significance of coupled modes of SVD, including parallel analysis, nonparametric bootstrap, random-phase test, and a new rule named modified parallel analysis. A numerical experiment is conducted to quantitatively compare the performance of the four rules in judging whether a coupled mode of SVD is significant as parameters such as the sample size, the number of grid points, and the signal-to-noise ratio vary.rnThe results show that the four rules perform better with lower ratio of the number of grid points to sample size. Modified parallel analysis and nonparametric bootstrap perform best to abandon the spurious coupled modes, but the latter is better than the former to retain the significant coupled modes when the sample size is not much larger than the number of grid points. Parallel analysis and random-phase test are robust to abandon the spurious coupled modes only when either (1) the observations at the grid points are spatially uncorrelated, or (2) the coupled signal is very strong for parallel analysis and is not weak for random-phase test. The reasons affecting the accuracy of the test rules are discussed.
机译：本文阐明了奇异值分解分析（SVD）的显着性检验的本质，并研究了四个检验SVD耦合模式的显着性的规则，包括并行分析，非参数自举，随机相位检验以及名为“修改”的新规则。并行分析。进行数值实验以定量比较这四个规则的性能，以判断SVD的耦合模式是否随样本大小，网格点数和信噪比等参数的变化而显着。结果表明，这四个规则在网格点数量与样本大小之比较低的情况下表现更好。修改后的并行分析和非参数引导程序可以最好地放弃虚假耦合模式，但是当样本大小不大于网格点数时，后者比保留前者更好地保留了重要的耦合模式。仅当以下两种情况之一时，并行分析和随机相位测试才能够可靠地放弃虚假耦合模式：（1）网格点处的观测值在空间上不相关，或者（2）耦合信号对于并行分析非常强，而对于随机信号则不弱相测试。讨论了影响测试规则准确性的原因。

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