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首页> 外文期刊>Nonlinear processes in geophysics >Statistical inference from atmospheric time series: Detecting trends and coherent structures
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Statistical inference from atmospheric time series: Detecting trends and coherent structures

机译：大气时间序列的统计推断：检测趋势和相干结构

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摘要

Standard statistical methods involve strong assumptions that are rarely met in real data, whereas resampling methods permit obtaining valid inference without making questionable assumptions about the data generating mechanism. Among these methods, subsampling works under the weakest assumptions, which makes it particularly applicable for atmospheric and climate data analyses. In the paper, two problems are addressed using subsampling: (1) the construction of simultaneous confidence bands for the unknown trend in a time series that can be modeled as a sum of two components: deterministic (trend) and stochastic (stationary process, not necessarily an i.i.d. noise or a linear process), and (2) the construction of confidence intervals for the skewness of a nonlinear time series. Non-zero skewness is attributed to the occurrence of coherent structures in turbulent flows, whereas commonly employed linear time series models imply zero skewness.
机译：标准统计方法涉及在实际数据中很少满足的强大假设，而重采样方法可在不对数据生成机制做出可疑假设的情况下获得有效推断。在这些方法中，子采样在最弱的假设下进行，这使其特别适用于大气和气候数据分析。在本文中，使用子采样解决了两个问题：（1）构建时间序列中未知趋势的同时置信带，可以将其建模为两个组成部分的总和：确定性（趋势）和随机性（平稳过程，而不是必然是iid噪声或线性过程），以及（2）构造非线性时间序列偏度的置信区间。非零偏度归因于湍流中相干结构的出现，而常用的线性时间序列模型暗示零偏度。

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