Asymptotic Statistics

Invited papers.- Procedures for the detection of multiple changes in series of independent observations.- Probing for information in two-stage stochastic programming and the associated consistency.- Outliers and switches in time series.- Fréchet differentiability and robust estimation.- Stein predictors and prediction regions.- Perpetuities and random equations.- On recent developments in the theory of set-indexed processes.- Regression rank scores scale statistics and studentization in linear models.- On an argmax-distribution connected to the Poisson process.- Asymptotic normality of regression M-estimators: Hadamard differentiability approaches.- Contributed papers.- Asymptotic theory for regression quantile estimators in the heteroscedastic regression model.- Nonnegative moving-average models.- Biased samples from a search and the asymptotic behaviour of Bayesian models.- A modification of least squares with high efficiency and high breakdown point in linear regression.- Significance of differences of estimates.- Adaptiveness in time series models.- Kernel estimators of integrated squared density derivatives in non-smooth cases.- Curve selection: A nonparametric approach.- Complete convergence.- Tests for heteroscedasticity based on regression quan-tiles and regression rank scores.- Parameter estimation by projecting on structural statistical models.- Some remarks on the joint estimation of the index and the scale parameter for stable processes.- Asymptotic behaviour of the error probabilities in the pseudo-likelihood ratio test for Gibbs-Markov distributions.- Detection of change in variance.- Shrinkage of maximum likelihood estimator of multivariate location.- Almost sure invariance principles for V- and U-statistics based on weakly dependent random variables.-On stability in two-stage stochastic nonlinear programming.- Spread inequality and efficiency of first and second order.- Confidence intervals for regression quantiles.- Spatial quantiles and their

Peter Mandl ist Professor für Wirtschaftsinformatik an der Hochschule München mit den Spezialgebieten Verteilte Systeme, Datenkommunikation und Betriebssysteme.