Computational Experience With Confidence Regions and Confidence Intervals for Nonlinear Least Squares

Abstract

We present the results of a Monte Carlo study of the leading methods for constructing approximate confidence regions and confidence intervals for parameters estimated by nonlinear least squares. We examine three variants of the linearization method, the likelihood method, and the lack-of-fit method. The linearization method is computationally inexpensive, produces easily understandable results, and is widely used in practice. The likelihood and lack-of-fit methods are much more expensive and more difficult to report. In our tests, both the likelihood and lack-of-fit methods perform very reliably. All three variants of the linearization method, however, often grossly underestimate confidence regions and sometimes significantly underestimate confidence intervals. The linearization method variant based solely on the Jacobian matrix appears preferable to the two variants that use the full Hessian matrix because it is less expensive, more numerically stable, and at least as accurate. The Bates and Watts curvature measures are consistent with our results.