If you need an accessible version of this item please contact JSTOR User Support

Comparison of Data-Driven Bandwidth Selectors

Byeong U. Park and J. S. Marron
Journal of the American Statistical Association
Vol. 85, No. 409 (Mar., 1990), pp. 66-72
DOI: 10.2307/2289526
Stable URL: http://www.jstor.org/stable/2289526
Page Count: 7
  • Download PDF
  • Cite this Item

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 need an accessible version of this item please contact JSTOR User Support
Comparison of Data-Driven Bandwidth Selectors
Preview not available

Abstract

This article compares several promising data-driven methods for selecting the bandwidth of a kernel density estimator. The methods compared are least squares cross-validation, biased cross-validation, and a plug-in rule. The comparison is done by asymptotic rate of convergence to the optimum and a simulation study. It is seen that the plug-in bandwidth is usually most efficient when the underlying density is sufficiently smooth, but is less robust when there is not enough smoothness present. We believe the plug-in rule is the best of those currently available, but there is still room for improvement.

Page Thumbnails

  • Thumbnail: Page 
66
    66
  • Thumbnail: Page 
67
    67
  • Thumbnail: Page 
68
    68
  • Thumbnail: Page 
69
    69
  • Thumbnail: Page 
70
    70
  • Thumbnail: Page 
71
    71
  • Thumbnail: Page 
72
    72