# Progress in data-based bandwidth selection for kernel density estimation

@article{Jones1996ProgressID, title={Progress in data-based bandwidth selection for kernel density estimation}, author={Chris Jones and J. S. Marron and Simon J. Sheather}, journal={Computational Statistics}, year={1996}, volume={11}, pages={337-381} }

We review the extensive recent literature on automatic, data-based selection of a global smoothing parameter in univariate kernel density estimation. Proposals are presented in a unified framework, making considerable reference to their theoretical properties as we go. The results of a major simulation study of the practical performance of many of these methods are summarised. Also, our remarks are further consolidated by describing a small portion of our practical experience on real datasets…

## 181 Citations

Bandwidth Selection Methods for Kernel Density Estimation - A Review of Performance

- Computer Science
- 2010

Existing methods are reviewed and compared on a set of designs that exhibits features like few bumps and exponentially falling tails concentrating thereby on small and moderate sample sizes to help solve practical issues like fully automatic procedures, implementation and performance.

Bandwidth selection for kernel density estimation: a review of fully automatic selectors

- Computer Science
- 2013

This work reviews existing methods and compares them on a set of designs that exhibit few bumps and exponentially falling tails and finds that a mixture of simple plug-in and cross-validation methods produces bandwidths with a quite stable performance.

Bandwidth Selection in Kernel Density Estimation

- Computer Science
- 2010

A new bandwidth selector has promising behavior with respect to the visual error criterion, especially in the cases of limited sample sizes, and a new data-driven bandwidth selector is proposed which is thought to be without these drawbacks.

Bandwidth Selection in

- Computer Science
- 2007

This paper summarizes the most important arguments for each criterion and gives an overview over the existing bandwidth selection methods and it will be shown how the presented bandwidth selectors can be implemented in a much faster way.

Cross‐validation Bandwidth Matrices for Multivariate Kernel Density Estimation

- Computer Science
- 2005

The results suggest that SCV for full bandwidth matrices is the most reliable of the CV methods, and observe that experience from the univariate setting can sometimes be a misleading guide for understanding bandwidth selection in the multivariate case.

Exploring the Use of Variable Bandwidth Kernel Density Estimators

- Computer Science
- 2003

The use of one implementation of a variable kernel estimator in conjunction with several rules and procedures for bandwidth selection applied to several real datasets showed fewer modes than those chosen by the Silverman test, especially those distributions in which multimodality was caused by several noisy minor modes.

A Brief Survey of Bandwidth Selection for Density Estimation

- Engineering
- 1996

Abstract There has been major progress in recent years in data-based bandwidth selection for kernel density estimation. Some “second generation” methods, including plug-in and smoothed bootstrap…

Practical bandwidth selection in deconvolution kernel density estimation

- Computer ScienceComput. Stat. Data Anal.
- 2004

Practical aspects of kernel smoothing for binary regression and density estimation

- Mathematics
- 1998

This thesis explores the practical use of kernel smoothing in three areas: binary regression, density estimation and Poisson regression sample size calculations.
Both nonparametric and…

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