An Empirical Study of Jackknife-Constructed Confidence Regions in Nonlinear Regression

Abstract

A jack knife procedure for constructing confidence regions in nonlinear regression is examined using Monte Carlo simulation. The jack knife promises to be asymptotically double-edged, being both independent of linearizing approximations to the regression surface and insensitive to specification of the error distribution. For moderate sample sizes the jack knife cannot be trusted in establishing joint confidence regions.

KEY WORDS:

• Estimation
• Monte Carlo
• Least squares
• Jack knife
• Nonlinear regression
• Confidence regions