Fast Algorithm for Choosing Blur Coefficients in Multidimensional Kernel Probability Density Estimates

被引：3
|
作者：
Lapko, A. V. [1 ,2 ]
Lapko, V. A. [1 ,2 ]
机构：
[1] Russian Acad Sci, Siberian Branch, Inst Computat Modeling, Krasnoyarsk, Russia
[2] Reshetnev Siberian State Univ Sci & Technol, Krasnoyarsk, Russia
来源：
MEASUREMENT TECHNIQUES | 2019年 / 61卷 / 10期
基金：
俄罗斯基础研究基金会;
关键词：
non-parametric estimation of multidimensional probability density; choice of blur coefficients; Rosenblatt-Parzen estimate; fast optimization algorithm; asymptotic properties; multidimensional data analysis; BANDWIDTH SELECTION; CROSS-VALIDATION;
D O I：
10.1007/s11018-019-01536-x
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
A method is proposed for quickly choosing the blur coefficients of kernel functions in a non-parametric estimate of a multidimensional probability density of Rosenblatt-Parzen type. The technique is based on the analysis of the asymptotic properties of a multidimensional probability density estimate. The properties of the fast algorithm for choosing the blur coefficients of a kernel probability density estimate are investigated.
引用
收藏
页码：979 / 986
页数：8
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