Nonlinear prediction by kriging, with application to noise cancellation. (Q1566660)

A semi-parametric approach based on kriging is suggested for nonlinear prediction. It does not rely on any specific model structure, which makes the approach much more flexible than those based on parametric behavioural models. At the same time, accurate predictions are obtained for extremely short-training sequences. Various examples are presented to illustrate the robustness of the method, with a discussion of the prior choices concerning the parametric part of the model. An application on real data is considered in the context of a noise-cancellation problem in underwater acoustics.