Rank-based inference methods are applied in various disciplines, typically when procedures relying on standard normal theory are not justifiable. Various specific rank-based methods have been developed for two and more samples and also for general factorial designs (e.g. Kruskal–Wallis test or...

Many estimators of the variance of the well-known unbiased and uniform most powerful estimator of the Mann–Whitney effect, are considered in the literature. Some of these estimators are only valid in cases of no ties or are biased in small sample sizes where the amount of bias is not...

Many estimators of the variance of the well-known unbiased and uniform most powerful estimator of the Mann–Whitney effect, are considered in the literature. Some of these estimators are only valid in cases of no ties or are biased in small sample sizes where the amount of bias is not...

The purpose of this paper is to propose a procedure for testing the equality of several regression curves fi in nonparametric regression models when the noise is inhomogeneous. This extends work of Dette and Neumeyer (2001) and it is shown that the new test is asymptotically uniformly more...

We present a new approach to handle dependencies within the general framework of case-control designs, illustrating our approach by a particular application from the field of genetic epidemiology. The method is derived for parent-offspring trios, which will later be relaxed to more general...

During the past the convergence analysis for linear statistical inverse problems has mainly focused on spectral cut-off and Tikhonov type estimators. Spectral cut-off estimators achieve minimax rates for a broad range of smoothness classes and operators, but their practical usefulness is limited...

Uniform confidence bands for densities f via nonparametric kernel estimates were first constructed by Bickel and Rosenblatt [Ann. Statist. 1, 1071.1095]. In this paper this is extended to confidence bands in the deconvolution problem g = f for an ordinary smooth error density . Under certain...

The computation of robust regression estimates often relies on minimization of a convex functional on a convex set. In this paper we discuss a general technique for a large class of convex functionals to compute the minimizers iteratively which is closely related to majorization-minimization...

The threshold vector error correction model is a popular tool for the analysis of spatial price transmission and market integration. In the literature, the profi le likelihood estimator is the preferred choice for estimating this model. Yet, in certain settings this estimator performs poorly. In...