Adjustment of Prediction Intervals in Nonlinear Regression

By treating the nonlinear model as if it were linear in the parameterization θ in the neighbourhood of the least squares estimate θC, two‐sided nominally‐ q ‐prediction intervals can be constructed by applying the usual linear model theory. The quadratic approximation of the expected coverage of the prediction intervals is derived for a p ‐parameter nonlinear model. An adjustment of the nominally‐ q ‐prediction intervals is proposed. It is shown that, to the extent that quadratic approximation is adequate, the actual expected coverage of the adjusted prediction intervals is q .