Hong Pi and Carsten Peterson
Estimating nonlinear regression errors without doing regression
arXiv:1404.3219 [LU TP 94-19]

A method for estimating nonlinear regression errors and their distributions without performing regression is presented. Assuming continuity of the modeling function the variance is given in terms of conditional probabilities extracted from the data. For N data points the computational demand is N2.The method is successfully illustrated with data generated by the Ikeda and Lorentz maps augmented with noise. As a by-product the embedding dimensions of these maps are extracted. Comparing the predicted residual errors with those from linear models provides a signal for nonlinearity.

LU TP 94-19