Owing to their extensive application in the field of robust estimation, the derivation of the distributions of linear combinations of order statistics or L-statistics and the computation of their moments has been approached in several ways. In this paper we use the properties of divided differences to obtain expressions for moments of some order statistics which arise as special cases of L-stat...

We propose a new statistics for the detection of differentially expressed genes when the genes are activated only in a subset of the samples. Statistics designed for this unconventional circumstance has proved to be valuable for most cancer studies, where oncogenes are activated for a small number of disease samples. Previous efforts made in this direction include cancer outlier profile analysi...

The word robustness is frequently used in the literature, and is often stated with completely di®erent meaning. In this contribution robustness means to reduce the in°uence of \unusual" observations on statistical estimates. Such observations are frequently denoted as outliers, and are often thought to be extreme values caused by measurement or transcription errors. However, the notion of outli...

1 Abstract We will develop conndence intervals for linear regression coeecients when the para-metric model is violated by the presence of a fraction of outliers and high leverage data points. Our method will be based on a robustiied bootstrap technique. Unlike the classical bootstrap, our robust bootstrap does not produce unduly heavy tails or extreme re-sampled statistics when the original sam...

A task of filtering (denoising) of high-pass signals corrupted by heavy tail (α-stable) noise is considered. For this purpose, several techniques based on the robust DFT forms have been proposed recently. In this paper we study various approaches for design of the robust DFT and compare their performance for frequency-modulated (FM) signals. In particular, we consider the DFT estimates based on...

EEG single-trial analysis requires methods that are robust against noise and disturbance. In this contribution, based on the framework of robust statistics, we propose a simple modification of common spatial patterns (CSP) by robustifying covariance estimators against outlying trials caused for example by artifacts. We tested the proposed robust filters with EEG recordings from 80 subjects and ...