
AbstractBy treating the nonlinear model as if it were linear in the parameterization θ in the neighbourhood of the least squares estimate θ, we construct two‐sided nominally‐q‐prediction intervals by applying the usual linear model theory. The derivation of the truncated series expansion of the expected coverage of the prediction intervals at a feasible value of the parameter vector is described. The quadratic approximation of the expected coverage is then obtained for a two‐parameter nonlinear model. Finally we show how we may construct the prediction intervals when a certain type of nonlinear transformation of the parameter vector has been applied.
truncated series expansion, least squares estimate, two-sided nominally-\(q\)-prediction intervals, local linearization, quadratic approximation, General nonlinear regression, expected coverage, parameterization, two-parameter nonlinear model, nonlinear transformations
truncated series expansion, least squares estimate, two-sided nominally-\(q\)-prediction intervals, local linearization, quadratic approximation, General nonlinear regression, expected coverage, parameterization, two-parameter nonlinear model, nonlinear transformations
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