摘要翻译：
平滑方法和SiZer是发现数据中有统计学意义的结构的一种有用的统计工具。基于计算机视觉文献中最初提出的尺度空间思想，SiZer（导数的显著过零点）是一种图形设备，用于评估哪些观察到的特征是“真正存在的”，哪些只是虚假的采样伪像。在本文中，我们在时间序列分析中发展了类似SiZer的思想，以解决趋势的重要性这一重要问题。这不是一个简单的扩展，因为一个数据集不包含区分“趋势”和“依赖”所需的信息。提出了一种新的可视化方法，它向统计人员显示了可用的权衡范围。仿真和实际数据结果表明了该方法的有效性。
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英文标题：
《SiZer for time series: A new approach to the analysis of trends》
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作者：
Vitaliana Rondonotti, J. S. Marron, Cheolwoo Park
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最新提交年份：
2007
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分类信息：

一级分类：Statistics        统计学
二级分类：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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英文摘要：
  Smoothing methods and SiZer are a useful statistical tool for discovering statistically significant structure in data. Based on scale space ideas originally developed in the computer vision literature, SiZer (SIgnificant ZERo crossing of the derivatives) is a graphical device to assess which observed features are `really there' and which are just spurious sampling artifacts. In this paper, we develop SiZer like ideas in time series analysis to address the important issue of significance of trends. This is not a straightforward extension, since one data set does not contain the information needed to distinguish `trend' from `dependence'. A new visualization is proposed, which shows the statistician the range of trade-offs that are available. Simulation and real data results illustrate the effectiveness of the method.
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PDF链接：
https://arxiv.org/pdf/706.419