英文标题：
《Detrended fluctuation analysis as a regression framework: Estimating
  dependence at different scales》
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作者：
Ladislav Kristoufek
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最新提交年份：
2015
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英文摘要：
  We propose a framework combining detrended fluctuation analysis with standard regression methodology. The method is built on detrended variances and covariances and it is designed to estimate regression parameters at different scales and under potential non-stationarity and power-law correlations. The former feature allows for distinguishing between effects for a pair of variables from different temporal perspectives. The latter ones make the method a significant improvement over the standard least squares estimation. Theoretical claims are supported by Monte Carlo simulations. The method is then applied on selected examples from physics, finance, environmental science and epidemiology. For most of the studied cases, the relationship between variables of interest varies strongly across scales.
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中文摘要：
我们提出了一个结合去趋势波动分析和标准回归方法的框架。该方法建立在去趋势方差和协方差的基础上，设计用于在不同尺度、潜在的非平稳性和幂律相关性下估计回归参数。前者允许从不同的时间角度区分一对变量的影响。后者使该方法比标准最小二乘估计有了显著的改进。理论主张得到了蒙特卡罗模拟的支持。然后将该方法应用于从物理学、金融学、环境科学和流行病学中选出的例子。在大多数研究案例中，感兴趣的变量之间的关系在不同的尺度上有很大差异。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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一级分类：Physics        物理学
二级分类：Data Analysis, Statistics and Probability        数据分析、统计与概率
分类描述：Methods, software and hardware for physics data analysis: data processing and storage; measurement methodology; statistical and mathematical aspects such as parametrization and uncertainties.
物理数据分析的方法、软硬件：数据处理与存储；测量方法；统计和数学方面，如参数化和不确定性。
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