It is proved that nonparametric autoregression is asymptotically equivalent in the sense of Le Cam's deficiency distance to nonpara-metric regression with random design as well as with regularâ€¦ (More)

1 1 1. Introduction Autoregressive models form an important class of processes in time series analysis. A nonparametric version of these models was introduced by Jones (1978). To allow forâ€¦ (More)

Wavelets have motivated development of a host of new ideas in nonparametric regression smoothing. Here we apply the tool of exact risk analysis, to understand the small sample behavior of waveletâ€¦ (More)

We derive an approximation of a density estimator based on weakly dependent random vectors by a density estimator built from independent random vectors We construct on a su ciently rich probabilityâ€¦ (More)

We devise a new method of estimating a distribution in a deconvolution model with panel data and an unknown distribution of the additive errors. We prove strong consistency under a minimal conditionâ€¦ (More)

We develop a method of estimating change{points of a function in the case of indirect noisy observations. As two paradigmatic problems we consider de-convolution and errors-in-variables regression.â€¦ (More)

New goodness-of-fit tests for Markovian models in time series analysis are developed which are based on the difference between a fully nonparametric estimate of the one-step transition distributionâ€¦ (More)

Degenerate U and V -statistics play an important role in the field of hypothesis testing since numerous test statistics can be formulated in terms of these quantities. Therefore, consistent bootstrapâ€¦ (More)

Knowledge about the distribution of a statistical estimator is important for various purposes, such as the construction of confidence intervals for model parameters or the determination of criticalâ€¦ (More)