  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. Comparing the
predicted residual errors with those derived from a linear model assumption
provides a signal for nonlinearity. The method is successfully illustrated with
data generated by the Ikeda and Lorenz maps augmented with noise. As a
by-product the embedding dimensions of these maps are also extracted.
