A Complete Efficient FFT-Based Algorithm for Nonparametric Kernel Density Estimation

被引：0

作者：

Gramacki, Jaroslaw ^{[1
]}

Gramacki, Artur ^{[2
]}

机构：

[1] Univ Zielona Gora, Ctr Comp, Zielona Gora, Poland

[2] Univ Zielona Gora, Inst Control & Computat Engn, Zielona Gora, Poland

来源：

ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING, ICAISC 2017, PT II | 2017年 / 10246卷

关键词：

Multivariate kernel density estimation; Unconstrained bandwidth matrix; Fast Fourier Transform; Nonparametric estimation; ACCURACY;

D O I：

10.1007/978-3-319-59060-8_7

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Multivariate kernel density estimation (KDE) is a very important statistical technique in exploratory data analysis. Research on high performance KDE is still an open research problem. One of the most elegant and efficient approach utilizes the Fast Fourier Transform. Unfortunately, the existing FFT-based solution suffers from a serious limitation, as it can accurately operate only with the constrained (i. e., diagonal) multivariate bandwidth matrices. In the paper we propose a crucial improvement to this algorithm which results in relaxing the above mentioned limitation. Numerical simulation study demonstrates good properties of the new solution.

引用

页码：62 / 73

页数：12

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