通过经验分布函数的局部多项式逼近改进核估计方法及其在经验回归中的应用。
Improvement of the method of kernel estimation by local polynomial approximation of the empirical distribution function and its application to empirical regression.
作者信息
Schmerling S, Peil J
出版信息
Gegenbaurs Morphol Jahrb. 1986;132(1):29-35.
PMID:3710112
Abstract
The kernel estimation of an unknown density function derived from measured values has at least one disadvantage: the second central moment of the estimated density is greater than the sample variance. This is unsatisfactory. It is possible to reduce the difference between both variances by an improvement of the method of kernel estimation. But for improved GAUSS-kernels it is not possible to reduce it to zero.
摘要
从测量值推导出来的未知密度函数的核估计至少有一个缺点
估计密度的二阶中心矩大于样本方差。这是不令人满意的。通过改进核估计方法有可能减小两者方差之间的差异。但是对于改进的高斯核，不可能将其减小到零。
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