The Study of Different Types of Kernel Density Estimators

DOI

10.2991/icence-16.2016.67How to use a DOI?

Keywords

Kernel Density Estimation (KDE); MATLAB; Probability Density Estimation(PDE); Clustering Algorithm Construction

Abstract

One of the most important method of estimating and graphing the underlying density is kernel density estimation (KDE). In this paper, we present basic knowledge of KDE, and simulations were carried out which compare three bandwidth selection methods [Normal rule of thumb (NROT), Least squares cross-validation (LSCV), and Biased cross-validation (BCV)]. Four types of kernel (Standard Normal, Biweight, Laplacian, Rational Quadratic and Circular) are chosen to do the simulation. Results shows that overall LSCV performs best.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - MingE Sha
AU  - Yonggang Xie
PY  - 2016/09
DA  - 2016/09
TI  - The Study of Different Types of Kernel Density Estimators
BT  - Proceedings of the 2nd International Conference on Electronics, Network and Computer Engineering (ICENCE 2016)
PB  - Atlantis Press
SP  - 336
EP  - 340
SN  - 2352-538X
UR  - https://doi.org/10.2991/icence-16.2016.67
DO  - 10.2991/icence-16.2016.67
ID  - Sha2016/09
ER  -