Frank Hampel

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The background of the paper is the empirical fact that the fundamental assumption of independence in statistics is generally false and has to be replaced by long-range dependence. Since this is far from generally known, some evidence of it is reviewed. It can easily be seen that long-range dependence has disastrous eeects on the naive statistical treatment(More)
A smoothing principle for M-estimators is proposed. The smoothing depends on the sample size so that the resulting smoothed M-estimator coincides with the initial M-estimator when n → ∞. The smoothing principle is motivated by an analysis of the requirements in the proof of the Cramér-Rao bound. The principle can be applied to every M-estimator. A(More)
The paper contains some general remarks on the high art of data analysis, some philosophical thoughts about classification, a partial review of outliers and robustness from the point of view of applications, including a discussion of the problem of model choice, and a review of several aspects of robust estimation of covariance matrices, including the(More)
About 25 years ago, Peter Bickel, Peter Huber and the author were invited to join John Tukey and spend the academic year 1970/71 at Princeton University in order to make a cooperative eeort for progress in robust statistics. What later was called the \Princeton robustness year," became widely known for its big Monte Carlo study of \robust location estimates(More)