二元极值Copula函数的相关函数的N-W核回归估计
N-W Kernel Regression Estimation for Correlation Function of Bivariate Extremes Copula Function
DOI: 10.12677/SA.2018.72027, PDF,    国家自然科学基金支持
作者: 蒋晓艺*, 张浩敏, 梁丽芳：桂林理工大学理学院，广西 桂林
关键词: 极值Copula函数；相关函数；N-W核回归估计；Extremes Copula Function； Correlation Function； N-W Kernel Regression Estimator
摘要: 本文利用核回归估计方法对二元极值Copula函数的相关函数进行估计。构建了相关函数的N-W核回归估计。在选择最优带宽的前提下，通过数值模拟对比了N-W核回归估计与OLS估计。数值模拟的结果显示N-W核回归估计在一定情况下较之于OLS估计更具有稳定性，是一种相对较优的相关函数非参数估计方法。
Abstract: This paper gives an estimate of correlation function for bivariate extremes Copula model using kernel regression method. A N-W kernel regression estimator is constructed and we prove that the estimator is asymptotically unbiased. Based on selection of the optimal bandwidth, we compare the N-W kernel regression estimation and OLS estimation by numerical simulation. The result shows that the N-W kernel regression estimator is more stable than the OLS estimator. So, the N-W kernel regression estimation is a relatively favourable non-parametric method.
文章引用：蒋晓艺, 张浩敏, 梁丽芳. 二元极值Copula函数的相关函数的N-W核回归估计[J]. 统计学与应用, 2018, 7(2): 234-240. https://doi.org/10.12677/SA.2018.72027

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