publication . Other literature type . Article . 1992

Variable kernel density estimation

Martin Hazelton;
Open Access English
  • Published: 01 Sep 1992
  • Publisher: The Institute of Mathematical Statistics
Abstract
Summary This paper considers the problem of selecting optimal bandwidths for variable (sample-point adaptive) kernel density estimation. A data-driven variable bandwidth selector is proposed, based on the idea of approximating the log-bandwidth function by a cubic spline. This cubic spline is optimized with respect to a cross-validation criterion. The proposed method can be interpreted as a selector for either integrated squared error (ISE) or mean integrated squared error (MISE) optimal bandwidths. This leads to reflection upon some of the differences between ISE and MISE as error criteria for variable kernel estimation. Results from simulation studies indicate...
Subjects
free text keywords: Kernel estimators, adaptive estimation, nearest-neighbor estimators, balloongrams, nonparametric smoothing, 62G05, 62G20, Kernel density estimation, Variable kernel density estimation, Spline (mathematics), Estimator, Multivariate kernel density estimation, Mathematics, Kernel (linear algebra), Mean squared error, Kernel (statistics), Statistics, Econometrics, Statistics, Probability and Uncertainty, Statistics and Probability, Kernel regression, Kernel embedding of distributions, Radial basis function kernel, Kernel principal component analysis
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