Asymptotically Optimal Bandwidth Selection for Kernel Density Estimators from Randomly Right-Censored Samples.

Abstract

This paper makes two important contributions to the theory of bandwidth selection for kernel density estimators under right censorship. First, an asymptotic representation of the integrated squared error into easily understood variance and squared bias components is given. Second, it is shown that if the bandwidth is chosen by the data-based method of least squares cross-validation, then it is asymptotically optimal in a compelling sense. A by-product of the first part is an interesting comparison of the two most popular kernel estimators. Keywords: Nonparametric density estimation; Smoothing parameter.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1986
Accession Number
ADA168941

Entities

People

  • J. S. Marron
  • William J. Padgett

Organizations

  • University of South Carolina

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Air Force
  • Bandwidth
  • Censorship
  • Computations
  • Consistency
  • Data Sets
  • Distribution Functions
  • Estimators
  • Multivariate Analysis
  • Probability
  • Probability Density Functions
  • Random Variables
  • South Carolina
  • Statistics
  • Stochastic Processes
  • Validation

Fields of Study

  • Mathematics

Readers

  • Statistical inference.