We propose weighted repeated median filters and smoothers for robust non-parametric regression in general and for … preserve discontinuities (shifts) in the underlying regression function (the signal) in the presence of local linear trends … properties for distinguishing between outlier sequences and long-term shifts. Robust smoothers based on weighted L1- regression …

change of the coefficients of a linear regression model with long-memory disturbances. It turns out that the asymptotic null …

In the common nonparametric regression model we consider the problem of constructing optimal designs, if the unknown …

In the common nonparametric regression model we consider the problem of constructing optimal designs, if the unknown … are determined numerically and compared with the uniform design. -- smoothing spline ; nonparametric regression ; D- and G …

This paper studies robustness of bootstrap inference methods for instrumental variable regression models. In particular …

Additive modelling has been widely used in nonparametric regression to circumvent the "curse of dimensionality", by … reducing the problem of estimating a multivariate regression function to the estimation of its univariate components …

Additive modelling is known to be useful for multivariate nonparametric regression as it reduces the complexity of … problem to the level of univariate regression. This usefulness could be compromised if the data set was contaminated by … procedure for the additive component of the regression function , less sensitive to possible outliers in the sample. Our …