Comparison of methods to account for autocorrelation in correlation analyses of fish data
Comparison of methods to account for autocorrelation in correlation analyses of fish data
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DOI：
10.1139/f98-104
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发表时间：
1998-09
影响因子：
2.4
通讯作者：
Brian J. Pyper;R. Peterman
中科院分区：
文献类型：
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作者：
Brian J. Pyper;R. Peterman
鱼类补充和环境数据的自相关性会使相关分析中的统计推断复杂化。为了解决这个问题，研究人员通常要么调整假设检验程序（例如，调整自由度）以说明自相关性或在分析之前使用预白化或第一差分来去除自相关性。然而，调整假设检验程序的方法的有效性尚未得到充分的定量探讨。因此，我们通过蒙特卡罗模拟比较了几种调整方法，发现这些方法的修改版本使I型错误率保持在接近。相反，与调整测试程序相比，去除自相关的方法可以很好地控制I型错误率，但在某些情况下可能会增加II型错误率（无法检测到某些环境影响的概率），从而降低统计功效。具体来说，我们的蒙特卡罗模拟表明，预白化，特别是第一D…
Autocorrelation in fish recruitment and environmental data can complicate statistical inference in correlation analyses. To address this problem, researchers often either adjust hypothesis testing procedures (e.g., adjust degrees of freedom) to account for autocorrelation or remove the autocorrelation using prewhitening or first-differencing before analysis. However, the effectiveness of methods that adjust hypothesis testing procedures has not yet been fully explored quantitatively. We therefore compared several adjustment methods via Monte Carlo simulation and found that a modified version of these methods kept Type I error rates near . In contrast, methods that remove autocorrelation control Type I error rates well but may in some circumstances increase Type II error rates (probability of failing to detect some environmental effect) and hence reduce statistical power, in comparison with adjusting the test procedure. Specifically, our Monte Carlo simulations show that prewhitening and especially first-d...