Séance Séminaire

Séminaire de Probabilités et Statistique

Nonparametric regression estimation of noisy data

We are interested in estimating the regression function non-parametrically when the data are measured with error. We study the behavior of the kernel density and regression estimators when the data are noisy. This is less standard than classical density and regression estimation problems. We applied these estimations to improve time series prediction in some situations and case of finite dimensional or functional data.