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Comparison Between Empirical Mode Decomposition and Local Linear Quantile Regression in the Presence of Boundaries

机译:经验模式分解与局部边界存在下局部线性定位回归的比较

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摘要

Quantile regression has been explored extensively by many researchers in the last two decades. This branch of regression provides a thorough and satisfactory approach to the analysis of regression models. Nonparametric quantile regressions can sometimes produce much valuable information concerning departures from standard model assumptions (like nonlinearity and heteroscedasicity). However, a novel method known as empirical mode decomposition (EMD) is capable of providing a good description of the non-stationary and nonlinear relationships among random variables. This study compares nonparametric quantile regressions (local linear quantile regressions) and EMD in the presence of boundaries. A simulation study is conducted to assess the numerical result.
机译:在过去的二十年中,许多研究人员已经广泛探讨了大分回归。这种回归分支提供了对回归模型的分析的彻底和令人满意的方法。非参数分位数回归有时可以产生关于从标准模型假设的偏离的非常有价值的信息(如非线性和异源性)。然而,一种称为经验模式分解(EMD)的新方法能够提供随机变量之间的非静止和非线性关系的良好描述。该研究将非参数分位数回归(本地线性分位数回归)和EMD在存在的存在下进行了比较。进行了模拟研究以评估数值结果。

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