World's Best Scientists 2026 revealed!
Peter Filzmoser

Peter Filzmoser

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Mathematics
Austria
2026

D-Index & Metrics

Mathematics

D-Index
63
Citations
19094
World Ranking
437
National Ranking
7

Research.com Recognitions

  • 2026 - Research.com Mathematics in Austria Leader Award
  • 2025 - Research.com Mathematics in Austria Leader Award
  • 2023 - Research.com Mathematics in Austria Leader Award

Overview

Peter Filzmoser is affiliated with TU Wien in Austria and has contributed extensively to the fields of Computer Science and Mathematics. Their research primarily spans statistics, probability, and computational methods, with a significant focus on advanced statistical techniques and geoscience applications.

The main areas of study include:

  • Computer Science
  • Mathematics

Subfields that characterize their work encompass:

  • Statistics and Probability
  • Artificial Intelligence
  • Statistics, Probability and Uncertainty
  • Signal Processing
  • Mechanics of Materials

Research topics covered by Peter Filzmoser are:

  • Advanced Statistical Methods and Models
  • Geochemistry and Geologic Mapping
  • Advanced Statistical Process Monitoring
  • Statistical Methods and Inference
  • Hydrocarbon exploration and reservoir analysis
  • Soil Geostatistics and Mapping
  • Blind Source Separation Techniques

Peter Filzmoser has published papers in several notable venues, including:

  • arXiv (Cornell University)
  • Mathematical Geosciences
  • Problemy Ekorozwoju
  • Austrian Journal of Statistics
  • Chemometrics and Intelligent Laboratory Systems

Recent publications demonstrate a focus on compositional data analysis, robust regression, and outlier detection. Selected papers are:

  • Comparison of zero replacement strategies for compositional data with large numbers of zeros, 2021, Chemometrics and Intelligent Laboratory Systems
  • Robust linear regression for high-dimensional data: An overview, 2020, Wiley Interdisciplinary Reviews Computational Statistics
  • Classical and Robust Regression Analysis with Compositional Data, 2020, Mathematical Geosciences
  • Analysing Pairwise Logratios Revisited, 2021, Mathematical Geosciences
  • Multivariate Outlier Detection in Applied Data Analysis: Global, Local, Compositional and Cellwise Outliers, 2020, Mathematical Geosciences

The scholar collaborates frequently with the following coauthors:

  • Karel Hron
  • Klaus Nordhausen
  • Christoph Muehlmann
  • Christopher Rieser
  • Pia Pfeiffer

Best Publications

  • Introduction to Multivariate Statistical Analysis in Chemometrics

    Kurt Varmuza;Peter Filzmoser

  • Background and threshold: critical comparison of methods of determination

    Clemens Reimann;Peter Filzmoser;Robert G. Garrett

  • Normal and lognormal data distribution in geochemistry: death of a myth. Consequences for the statistical treatment of geochemical and environmental data.

    C. Reimann;P. Filzmoser

  • Statistical data analysis explained

    Clemens Reimann;Peter Filzmoser;Robert G. Garrett;Rudolf Dutter

  • Factor analysis applied to regional geochemical data: problems and possibilities

    Clemens Reimann;Peter Filzmoser;Robert G. Garrett

  • Statistical data analysis explained : applied environmental statics with R

    Clemens Reimann;Peter Filzmoser;Robert G. Garrett;R. Dutter

  • Principal component analysis for compositional data with outliers

    Peter Filzmoser;Karel Hron;Clemens Reimann

  • Multivariate outlier detection in exploration geochemistry

    Peter Filzmoser;Robert G. Garrett;Clemens Reimann

  • Outlier identification in high dimensions

    Peter Filzmoser;Ricardo Maronna;Mark Werner

  • Repeated double cross validation

    Peter Filzmoser;Bettina Liebmann;Kurt Varmuza

  • An Object-Oriented Framework for Robust Multivariate Analysis

    Valentin Todorov;Peter Filzmoser

  • Univariate statistical analysis of environmental (compositional) data: Problems and possibilities

    Peter Filzmoser;Karel Hron;Clemens Reimann

  • robCompositions: An R-package for Robust Statistical Analysis of Compositional Data

    Matthias Templ;Matthias Templ;Karel Hron;Peter Filzmoser

  • Imputation of missing values for compositional data using classical and robust methods

    K. Hron;M. Templ;P. Filzmoser

  • Algorithms for Projection–Pursuit robust principal component analysis

    Christophe Croux;P Filzmoser;M. R Oliveira

  • Bayesian-multiplicative treatment of count zeros in compositional data sets

    Josep-Antoni Martín-Fernández;Karel Hron;Matthias Templ;Matthias Templ;Peter Filzmoser

  • Outlier Detection for Compositional Data Using Robust Methods

    Peter Filzmoser;Karel Hron

  • The concept of compositional data analysis in practice--total major element concentrations in agricultural and grazing land soils of Europe.

    Clemens Reimann;Peter Filzmoser;Karl Fabian;Karel Hron

  • The bivariate statistical analysis of environmental (compositional) data.

    Peter Filzmoser;Karel Hron;Clemens Reimann

  • Exploratory factor analysis revisited: How robust methods support the detection of hidden multivariate data structures in IS research

    Horst Treiblmaier;Peter Filzmoser

  • Algorithms for Projection-Pursuit Robust Principal Component Analysis

    Christophe Croux;Peter Filzmoser;M. Rosario Oliveira

Frequent Co-Authors

Clemens Reimann
Clemens Reimann Norwegian Geological Survey
Christophe Croux
Christophe Croux EDHEC Business School
Manfred Birke
Manfred Birke Federal Institute for Geosciences and Natural Resources
Philippe Négrel
Philippe Négrel French Geological Survey
Mike J. McLaughlin
Mike J. McLaughlin University of Adelaide
Trajče Stafilov
Trajče Stafilov Saints Cyril and Methodius University of Skopje
Enrico Dinelli
Enrico Dinelli University of Bologna
Annamaria Lima
Annamaria Lima University of Naples Federico II
Ewald Moser
Ewald Moser Medical University of Vienna
Stefano Albanese
Stefano Albanese University of Naples Federico II

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