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

Jackknife-Based Estimators and Confidence Regions in Nonlinear Regression

Jeffrey S. Simonoff and Chih-Ling Tsai
Technometrics
Vol. 28, No. 2 (May, 1986), pp. 103-112
DOI: 10.2307/1270446
Stable URL: http://www.jstor.org/stable/1270446
Page Count: 10
  • Download ($14.00)
  • Cite this Item
If you need an accessible version of this item please contact JSTOR User Support
Jackknife-Based Estimators and Confidence Regions in Nonlinear Regression
Preview not available

Abstract

The properties of several jackknife-based estimators are investigated in the context of the nonlinear regression model. These estimators are suggested to overcome lack of balance of the design and the nonlinearity of the model. It is shown that a reweighted estimator combined with a modified variance estimate provides a technique that is reasonably robust with respect to outliers, leverage points, and curvature effects. Several examples are presented to illustrate these properties.

Page Thumbnails

  • Thumbnail: Page 
103
    103
  • Thumbnail: Page 
104
    104
  • Thumbnail: Page 
105
    105
  • Thumbnail: Page 
106
    106
  • Thumbnail: Page 
107
    107
  • Thumbnail: Page 
108
    108
  • Thumbnail: Page 
109
    109
  • Thumbnail: Page 
110
    110
  • Thumbnail: Page 
111
    111
  • Thumbnail: Page 
112
    112