publication . Other literature type . Article . 1992

Variable Kernel Density Estimation

David W. Scott; George R. Terrell;
Open Access English
  • Published: 01 Sep 1992
  • Publisher: The Institute of Mathematical Statistics
Abstract
We investigate some of the possibilities for improvement of univariate and multivariate kernel density estimates by varying the window over the domain of estimation, pointwise and globally. Two general approaches are to vary the window width by the point of estimation and by point of the sample observation. The first possibility is shown to be of little efficacy in one variable. In particular, nearest-neighbor estimators in all versions perform poorly in one and two dimensions, but begin to be useful in three or more variables. The second possibility is more promising. We give some general properties and then focus on the popular Abramson estimator. We show that...
Subjects
free text keywords: Kernel estimators, adaptive estimation, nearest-neighbor estimators, balloongrams, nonparametric smoothing, 62G05, 62G20, Statistics, Probability and Uncertainty, Statistics and Probability, Kernel embedding of distributions, Kernel density estimation, Applied mathematics, Kernel regression, Multivariate kernel density estimation, Radial basis function kernel, Mathematics, Kernel principal component analysis, Kernel (statistics), Variable kernel density estimation, Statistics
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