Selection of the Blur Coefficient for Probability Density Kernel Estimates Under Conditions of Large Samples

被引：0

作者：

A. V. Lapko

V. A. Lapko

机构：

[1] Siberian Branch of the Russian Academy of Sciences,Institute of Computational Modeling

[2] Reshetnev Siberian State University of Science and Technology,undefined

来源：

Measurement Techniques | 2019年 / 62卷

关键词：

kernel probability density estimate; quick selection of blur factors; discretization of the range of values of a random variable; large volume statistical data;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

A fast algorithm is proposed for choosing the blur factors of kernel functions of a non-parametric probability density estimate under conditions of large-scale statistical data. It is shown that the basis of the algorithm is the result of a study of the asymptotic properties of a new kernel probability density estimate. The properties of the developed algorithm are analyzed and the method of its application is formulated.

引用

页码：383 / 389

页数：6

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