An Asymptotically Efficient Solution to the Bandwidth Problem of Kernel Density Estimation. Revision.

Abstract

A data-driven method of choosing the bandwidth, h, of a kernel density estimator is proposed. It is seen that this means of selecting h is asymptotically equivalent to taken the h that minimizes a certain weighted version of the mean integrated square error. Thus, for a given kernel function, the bandwidth can be chosen optimally without making precise smoothness assumptions on the underlying density. The proposed technique is a modification of cross-validation.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 1984
Accession Number
ADA149600

Entities

People

  • J. S. Marron

Organizations

  • University of North Carolina at Chapel Hill

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Bandwidth
  • Classification
  • Computations
  • Convergence
  • Data Analysis
  • Data Science
  • Distribution Theory
  • Estimators
  • Information Science
  • Intervals
  • Kernel Functions
  • North Carolina
  • Optimal Estimators
  • Order Statistics
  • Probability
  • Statistical Algorithms
  • Statistics

Fields of Study

  • Mathematics

Readers

  • Approximation Theory.
  • Neural Network Machine Learning.