A Nonlinear Approach to Linear Calibration Intervals

Abstract

We show that a nonlinear approach to single use calibration curves gives reasonable intervals. The nonlinear approach produces intervals even when the classical approach fails to do so. The intervals obtained with the nonlinear approach are always slightly shorter, but the coverage rate remains reasonable, as demonstrated in our simulations. The advantages of the nonlinear approach are that intervals are centered at the maximum likelihood estimator (MLE) and that they can be obtained using any standard statistical package which contains nonlinear regression methods.

• Confidence Intervals
• Estimation
• Inverse Regression
• Least Squares

Additional information

Notes on contributors

Edna Schechtman

Dr. Edna Schechtman is a Statistician in the Department of Industrial Engineering and Management. Her email address is [email protected].

Cliff Spiegelman

Dr. Cliff Spiegelman is a Professor in the Department of Statistics.