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Dissecting innovative trend analysis
Stochastic Environmental Research and Risk Assessment ( IF 3.9 ) Pub Date : 2020-05-04 , DOI: 10.1007/s00477-020-01797-x
Francesco Serinaldi , Fateh Chebana , Chris G. Kilsby

Investigating the nature of trends in time series is one of the most common analyses performed in hydro-climate research. However, trend analysis is also widely abused and misused, often overlooking its underlying assumptions, which prevent its application to certain types of data. A mechanistic application of graphical diagnostics and statistical hypothesis tests for deterministic trends available in ready-to-use software can result in misleading conclusions. This problem is exacerbated by the existence of questionable methodologies that lack a sound theoretical basis. As a paradigmatic example, we consider the so-called Şen’s ‘innovative’ trend analysis (ITA) and the corresponding formal trend tests. Reviewing each element of ITA, we show that (1) ITA diagrams are equivalent to well-known two-sample quantile-quantile (q–q) plots; (2) when applied to finite-size samples, ITA diagrams do not enable the type of trend analysis that it is supposed to do; (3) the expression of ITA confidence intervals quantifying the uncertainty of ITA diagrams is mathematically incorrect; and (4) the formulation of the formal tests is also incorrect and their correct version is equivalent to a standard parametric test for the difference between two means. Overall, we show that ITA methodology is affected by sample size, distribution shape, and serial correlation as any parametric technique devised for trend analysis. Therefore, our results call into question the ITA method and the interpretation of the corresponding empirical results reported in the literature.



中文翻译:

剖析创新趋势分析

调查时间序列趋势的性质是水文气候研究中最常见的分析之一。但是,趋势分析也被广泛滥用和滥用,通常会忽略其基本假设,从而妨碍了将其应用于某些类型的数据。图形诊断和统计假设检验在可立即使用的软件中可用的确定性趋势的机械应用可能导致误导性结论。由于存在缺乏可靠的理论基础的可疑方法,该问题更加严重。作为示例,我们考虑所谓的“创新”趋势分析(ITA)和相应的正式趋势测试。回顾ITA的每个元素,我们发现(1)ITA图等效于众所周知的两样本分位数-分位数(q-q)图;(2)当将ITA图应用于有限大小的样本时,它不会启用应该执行的趋势分析类型;(3)量化ITA图不确定性的ITA置信区间的表达在数学上是不正确的;(4)形式检验的表述也不正确,其正确版本等同于标准方法检验两种方法之间的差异。总体而言,我们显示出ITA方法论受样本大小,分布形状和序列相关性的影响,这是为趋势分析设计的任何参数技术。因此,我们的结果对ITA方法和文献中报道的相应经验结果的解释提出了质疑。(3)量化ITA图不确定性的ITA置信区间的表达在数学上是不正确的;(4)形式检验的表述也不正确,其正确版本等同于标准方法检验两种均值之间的差异。总体而言,我们显示出ITA方法论受样本大小,分布形状和序列相关性的影响,这是为趋势分析设计的任何参数技术。因此,我们的结果对ITA方法和文献中报道的相应经验结果的解释提出了质疑。(3)量化ITA图不确定性的ITA置信区间的表达在数学上是不正确的;(4)形式检验的表述也不正确,其正确版本等同于标准方法检验两种方法之间的差异。总体而言,我们显示出ITA方法论受样本大小,分布形状和序列相关性的影响,这是为趋势分析设计的任何参数技术。因此,我们的结果对ITA方法和文献中报道的相应经验结果的解释提出了质疑。(4)形式检验的表述也不正确,其正确版本等同于标准方法检验两种均值之间的差异。总体而言,我们显示出ITA方法论受样本大小,分布形状和序列相关性的影响,这是为趋势分析设计的任何参数技术。因此,我们的结果对ITA方法和文献中报道的相应经验结果的解释提出了质疑。(4)形式检验的表述也不正确,其正确版本等同于标准方法检验两种方法之间的差异。总体而言,我们显示出ITA方法论受样本大小,分布形状和序列相关性的影响,这是为趋势分析设计的任何参数技术。因此,我们的结果对ITA方法和文献中报道的相应经验结果的解释提出了质疑。

更新日期:2020-05-04
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