Improved Seasonal Mann–Kendall Tests for Trend Analysis in Water Resources Time Series
Improved Seasonal Mann–Kendall Tests for Trend Analysis in Water Resources Time Series
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DOI：
10.1007/978-1-4939-6568-7_10
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Ying Zhang;P. Cabilio;Khurram Nadeem
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文献类型：
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作者：
Ying Zhang;P. Cabilio;Khurram Nadeem
非参数统计程序通常用于分析水资源时间序列的趋势（第23章，Hipel和McLeod在水资源和环境系统的时间序列建模中。Elsevier，纽约，2005年[10]）。一种流行的方法是季节性Mann-Kendall tau检验，用于检测具有序列依赖性的季节性时间序列数据中的单调趋势（Hirsch和Slack in Water Resour Res 20（6）：727-732，1984 [12]）。然而，在文献中很少有严格的讨论，其有效性和替代品。本文对一族绝对正则过程，给出了季节性Mann-Kendall检验的渐近正态性，提出了一种Bootstrap抽样检验，并通过模拟研究了其性能。这些模拟比较了传统测试、上述自举版本以及斯皮尔曼的ρ偏相关的自举版本的性能。仿真结果表明，当季节效应为确定性时，两种Bootstrap检验的效果均优于传统检验，而当季节效应为随机性时，传统检验不能收敛到名义水平。这两种自举测试在精度和功耗方面的表现相似。
Nonparametric statistical procedures are commonly used in analyzing for trend in water resources time series (Chapter 23, Hipel and McLeod in Time series modelling of water resources and environmental systems. Elsevier, New York, 2005 [10]). One popular procedure is the seasonal Mann–Kendall tau test for detecting monotonic trend in seasonal time series data with serial dependence (Hirsch and Slack in Water Resour Res 20(6):727–732, 1984 [12]). However there is little rigorous discussion in the literature about its validity and alternatives. In this paper, the asymptotic normality of a seasonal Mann–Kendall test is determined for a large family of absolutely regular processes, a bootstrap sampling version of this test is proposed and its performance is studied through simulation. These simulations compare the performance of the traditional test, the bootstrapped version referred to above, as well as a bootstrapped version of Spearman’s rho partial correlation. The simulation results indicate that both bootstrap tests perform comparably to the traditional test when the seasonal effect is deterministic, but the traditional test can fail to converge to the nominal levels when the seasonal effect is stochastic. Both bootstrapped tests perform similarly to each other in terms of accuracy and power.