Access

You are not currently logged in.

Access your personal account or get JSTOR access through your library or other institution:

login

Log in to your personal account or through your institution.

If You Use a Screen Reader

This content is available through Read Online (Free) program, which relies on page scans. Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.

An Alternative Choice of Smoothing for Kernel-Based Density Estimates in Discrete Discriminant Analysis

Gerhard Tutz
Biometrika
Vol. 73, No. 2 (Aug., 1986), pp. 405-411
Published by: Oxford University Press on behalf of Biometrika Trust
DOI: 10.2307/2336217
Stable URL: http://www.jstor.org/stable/2336217
Page Count: 7
  • Read Online (Free)
  • Subscribe ($19.50)
  • Cite this Item
Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
An Alternative Choice of Smoothing for Kernel-Based Density Estimates in Discrete Discriminant Analysis
Preview not available

Abstract

The kernel method of estimating the cell probabilities of a multivariate categorical distribution, due to Aitchison & Aitken (1976), depends crucially on an unknown smoothing parameter λ. A method of estimating λ is introduced which is explicitly connected to multivariate discrimination. The method, based on maximization of the leaving-one-out estimator of the nonerror rate, is shown to be Bayes risk strongly consistent. An example is given to illustrate the application.

Page Thumbnails

  • Thumbnail: Page 
[405]
    [405]
  • Thumbnail: Page 
406
    406
  • Thumbnail: Page 
407
    407
  • Thumbnail: Page 
408
    408
  • Thumbnail: Page 
409
    409
  • Thumbnail: Page 
410
    410
  • Thumbnail: Page 
411
    411