Predictive performance of some nonparametric linear and nonl-inear smoothers for noisy data

E. Bee Dagum & A. Luati
The purpose of this study is to discuss the weighting systems of several linear and non linear smoothers and to evaluate their predictive performances when applied to noisy time series. On this regard, we illustrate with three Canadian leading indicators which are representative of larger sets of time series characterised by a low, medium and high signal to noise ratio. The smoothers discussed are: (a) loess (a locally weighted regression smoother), (b) Gaussian Kernel smoother,...