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

On Local Smoothing of Nonparametric Curve Estimators

Jianqing Fan, Peter Hall, Michael A. Martin and Prakash Patil
Journal of the American Statistical Association
Vol. 91, No. 433 (Mar., 1996), pp. 258-266
DOI: 10.2307/2291403
Stable URL: http://www.jstor.org/stable/2291403
Page Count: 9
  • 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
On Local Smoothing of Nonparametric Curve Estimators
Preview not available

Abstract

We develop new local versions of familiar smoothing methods, such as cross-validation and smoothed cross-validation, in the contexts of density estimation and regression. These new methods are locally adaptive in the sense that they capture smooth local fluctuations in the curve by using smoothly varying bandwidths that change as the character of the curve changes. Moreover, the new methods are accurate, easy to apply, and computationally expedient.

Page Thumbnails

  • Thumbnail: Page 
258
    258
  • Thumbnail: Page 
259
    259
  • Thumbnail: Page 
260
    260
  • Thumbnail: Page 
261
    261
  • Thumbnail: Page 
262
    262
  • Thumbnail: Page 
263
    263
  • Thumbnail: Page 
264
    264
  • Thumbnail: Page 
265
    265
  • Thumbnail: Page 
266
    266