Journal Article10.1080/01621459.1996.10476701
A Brief Survey of Bandwidth Selection for Density Estimation
TL;DR: In this article, the authors recommend a "solve-the-equation" plug-in bandwidth selector as being most reliable in terms of overall performance for kernel density estimation.
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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 techniques, have been developed that are far superior to well-known “first generation” methods, such as rules of thumb, least squares cross-validation, and biased cross-validation. We recommend a “solve-the-equation” plug-in bandwidth selector as being most reliable in terms of overall performance. This article is intended to provide easy accessibility to the main ideas for nonexperts.
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References
Density estimation for statistics and data analysis
Bernard W. Silverman
- 01 Jan 1986
TL;DR: The Kernel Method for Multivariate Data: Three Important Methods and Density Estimation in Action.
Density Estimation for Statistics and Data Analysis
TL;DR: Density estimation, as discussed in this book, is the construction of an estimate of the density function from the observed data from an unknown probability density function.
14.7K
•Book
Spline models for observational data
Grace Wahba
- 01 Mar 1990
TL;DR: In this paper, a theory and practice for the estimation of functions from noisy data on functionals is developed, where convergence properties, data based smoothing parameter selection, confidence intervals, and numerical methods are established which are appropriate to a number of problems within this framework.
6.9K
Multivariate Density Estimation, Theory, Practice and Visualization
TL;DR: Representation and Geometry of Multivariate Data.
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