Peter Filzmoser

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Why to read "yet another book" on statistical methods? This is a question that may arise among chemometricians, chemists, students or anyone using chemometric methods in their research or everyday routine work. However, this book is a very good contribution to a better understanding of statistics because it really captures the point of potential(More)
Analysis of resting-state networks using fMRI usually ignores high-frequency fluctuations in the BOLD signal - be it because of low TR prohibiting the analysis of fluctuations with frequencies higher than 0.25 Hz (for a typical TR of 2 s), or because of the application of a bandpass filter (commonly restricting the signal to frequencies lower than 0.1 Hz).(More)
Principal Component Analysis (PCA) is very sensitive in presence of outliers. One of the most appealing robust methods for principal component analysis uses the Projection-Pursuit principle. Here, one projects the data on a lower-dimensional space such that a robust measure of variance of the projected data will be maximized. The Projection-Pursuit based(More)
Different procedures to identify data outliers in geochemical data are reviewed and tested. The calculation of [mean+/-2 standard deviation (sdev)] to estimate threshold values dividing background data from anomalies, still used almost 50 years after its introduction, delivers arbitrary estimates. The boxplot, [median+/-2 median absolute deviation (MAD)](More)
A new method for multivariate outlier detection able to distinguish between extreme values of a normal distribution and values originating from a different distribution (outliers) is presented. To facilitate visualising multivariate outliers spatially on a map, the multivariate outlier plot, is introduced. In this plot different symbols refer to a distance(More)
The Maximum Likelihood Estimator (MLE) has commonly been used to estimate the unknown parameters in the finite mixture of distributions via the expectationmaximization (EM) algorithm. However, the MLE can be very sensitive to outliers in the data. Various approaches that have incorporated robustness in fitting mixtures and clustering are discussed. Special(More)
Systems projecting a continuous n-dimensional parameter space to a continuous m-dimensional target space play an important role in science and engineering. If evaluating the system is expensive, however, an analysis is often limited to a small number of sample points. The main contribution of this paper is an interactive approach to enable a continuous(More)
A computationally fast procedure for identifying outliers is presented, that is particularly effective in high dimensions. This algorithm utilizes simple properties of principal components to identify outliers in the transformed space, leading to significant computational advantages for high dimensional data. This approach requires considerably less(More)