PUBLICATIONS
Peter Filzmoser

All publications from TU Wien data base (+bibtex)

Books

• P. Filzmoser, K. Hron, and M. Templ. Applied Compositional Data Analysis. With Worked Examples in R. Springer Series in Statistics, Springer Nature Switzerland AG, Cham, Switzerland, 2018. ISBN: 978-3-319-96422-5. INFORMATION
  
• K. Varmuza and P. Filzmoser. Introduction to Multivariate Statistical Analysis in Chemometrics. Taylor & Francis - CRC Press, Boca Raton, FL, 2009. ISBN: 9781420059472. INFORMATION
  
• V. Todorov and P. Filzmoser. Multivariate Robust Statistics: Methods and Computation. Südwestdeutscher Verlag für Hochschulschriften, Saarbrücken, 2009. ISBN: 978-3-8381-0814-8.
  
• C. Reimann, P. Filzmoser, R.G. Garrett, and R. Dutter. Statistical Data Analysis Explained. Applied Environmental Statistics with R. John Wiley & Sons, Chichester, 2008. ISBN: 978-0-470-98581-6 (H/B). INFORMATION
  
• H. Fritz and P. Filzmoser. Plausibility of Databases and the Relation to Imputation Methods. VDM Verlag Dr. Müller, Saarbrücken, 2008. ISBN: 978-3-8364-5992-1.
  
• K. Adel, R. Dutter, H. Filzmoser und P. Filzmoser. Tiefenstrukturen der Sprache: Untersuchung regionaler Unterschiede mit statistischen Methoden. WUV-Universitaetsverlag, Wien, 1994.
  

Papers in Journals and Proceedings Volumes, Editorial work

2023

• P. Filzmoser and A. Mazak-Huemer. Massive data sets - is data quality still an issue? In: B. Vogel-Heuser and M. Wimmer (Eds.), Digital Transformation: Core Technologies and Emerging Topics from a Computer Science Perspective. Springer-Vieweg, Heidelberg, Germany. To appear.

2022

• M. Brandl, C.A. Hauzenberger, P. Filzmoser, and M.M. Martinez. Geochemical sourcing of chipped stone tools from Platia Magoula Zarkou. In: E. Alram-Stern, K. Gallis, and G. Toufexis (Eds.). Platia Magoula Zarkou. The Neolithic Period. Austrian Academy of Sciences Press, Austria, pp. 291-309, 2022. https://library.oapen.org/handle/20.500.12657/58537
• K. Facevicova, P. Filzmoser, and K. Hron. Compositional cubes: a new concept for multi-factorial compositions. Statistical Papers, 2022. doi.org/10.1007/s00362-022-01350-8
• G. Heiler, A. Hanbury, and P. Filzmoser. The impact of COVID-19 on relative changes in aggregated mobility using mobile-phone data. Austrian Journal of Statistics. To appear.
• K. Hron, A. Menafoglio, J. Palarea-Albaladejo, P. Filzmoser, R. Talska, and J.J. Egozcue. Weighting of parts in compositional data analysis: Advances and applications. Mathematical Geosciences, 54, 71-93, 2022. doi.org/10.1007/s11004-021-09952-y
• G.S. Monti and P. Filzmoser. A robust knockoff filter for sparse regression analysis of microbiome compositional data. Computational Statistics, 2022. doi.org/10.1007/s00180-022-01268-7
• G.S. Monti and P. Filzmoser. Robust logistic zero-sum regression for microbiome compositional data. Advances in Data Analysis and Classification, 16, 301-324, 2022. doi.org/10.1007/s11634-021-00465-4
• N. Mumic, O. Leodolter, A. Schwaiger, and P. Filzmoser. Scale invariant and robust pattern identification in univariate time series, with application to growth trend detection in music streaming data. In: A. Steland and K.-L. Tsui (Eds.), Artificial Intelligence, Big Data and Data Science in Statistics. Challenges and Solutions in Environmetrics, the Natural Sciences and Technology, Springer Nature Switzerland, Cham, pp. 25-50, 2022. doi.org/10.1007/978-3-031-07155-3
• I. Pavlu, P. Filzmoser, A. Menafoglio, and K. Hron. Classification of continuous distributional data using the logratio approach. In: P. Brito and S. Dias (Eds.) Analysis of Distributional Data. CRC Press, Boca Raton, USA, pp. 183-200, 2022. doi.org/10.1201/9781315370545
• P. Pfeiffer, B. Ronai, G. Vorlaufer, N. Dörr, and P. Filzmoser. Weighted LASSO variable selection for the analysis of FTIR spectra applied to the prediction of engine oil degradation. Chemometrics and Intelligent Laboratory Systems, 228, 104617, 2022. doi.org/10.1016/j.chemolab.2022.104617
• N. Piccolotto, M. Bögl, T. Gschwandtner, C. Muehlmann, K. Nordhausen, P. Filzmoser, and S. Miksch. TBSSvis: Visual analytics for temporal Blind Source Separation. Visual Informatics, 2022. doi.org/10.1016/j.visinf.2022.10.002
• N. Piccolotto, M. Bögl, C. Muehlmann, K. Nordhausen, P. Filzmoser, and S. Miksch. Visual parameter selection for spatial blind source separation. Computer Graphics Forum, 41(3), 157-168, 2022. doi.org/10.1111/cgf.14530
• P. Sarala, J.P. Lunkka, V. Sarajärvi, O. Sarala, and P. Filzmoser. Timing of glacial - non-glacial stages in Finland: an exploratory analysis of the OSL data. Arctic, Antarctic, and Alpine Research, 54(1), 428-442, 2022. doi.org/10.1080/15230430.2022.2117765
• P. Weltler, K. Rappersberger, P. Filzmoser, I. Vujic. The impact of the COVID-19 pandemic on melanoma diagnoses. JEADV Clinical Practice, 1(2), 122-125, 2022. doi.org/10.1002/jvc2.15

2021

• S. de la Rosa de Saa, M.A. Lubiano, B. Sinova, M.A. Gil, and P. Filzmoser. Location-free robust scale estimates for fuzzy data. IEEE Transactions on Fuzzy Systems, 29(6), 1682-1694, 2021. doi.org/10.1109/TFUZZ.2020.2984203
• J. de Sousa, K. Hron, K. Facevicova, and P. Filzmoser. Robust principal component analysis for compositional tables. Journal of Applied Statistics, 48(2), 214-233, 2021. doi.org/10.1080/02664763.2020.1722078
• M. Drastichova and P. Filzmoser. Factors of quality of life in a group of selected European Union and OECD countries. Problemy Ekorozwoju - Problems of Sustainable Development, 16(2), 75-93, 2021. doi.org/10.35784/pe.2021.2.09
• A.P. Duarte Silva, P. Brito, P. Filzmoser, and J.G. Dias. MAINT.Data: Modelling and analysing interval data in R. The R Journal, 13(2), 266-272, 2021. doi.org/10.32614/RJ-2021-074
• P. Filzmoser. Robust statistics. In: B.S.D. Sagar, Q. Cheng, J. McKinley, and F. Agterberg (Eds.): Earth Sciences Series. Encyclopedia of Mathematical Geosciences., 2021. doi.org/10.1007/978-3-030-26050-7_425-1
• P. Filzmoser, K. Hron, J.A. Martin-Fernandez, J. Palarea-Albaladejo, editors, Advances in Compositional Data Analysis. Festschrift in Honour of Vera Pawlowsky-Glahn, Springer Nature Switzerland, Cham, Switzerland, 2021. doi.org/10.1007/978-3-030-71175-7
• P. Filzmoser, K. Hron, and A. Menafoglio. Logratio approach to distributional modeling. In: A. Daouia and A. Ruiz-Gazen (Eds.), Advances in Contemporary Statistics and Econometrics, Festschrift for Christine Thomas. Springer, Cham, 2021, pp. 451-470. doi.org/10.1007/978-3-030-73249-3_23
• P. Filzmoser and K. Nordhausen. Robust linear regression for high-dimensional data: an overview. WIREs Computational Statistics, 13(4), e1524, 2021. doi.org/10.1002/wics.1524
• K. Hron, G. Coenders, P. Filzmoser, J. Palarea-Albaladejo, M. Famera, and T.M. Grygar. Analysing pairwise logratios revisited. Mathematical Geosciences, 53, 1643-1666, 2021. doi.org/10.1007/s11004-021-09938-w
• S. Lubbe, P. Filzmoser, and M. Templ. Comparison of zero replacement strategies for compositional data with large numbers of zeros. Chemometrics and Intelligent Laboratory Systems.. 210, 104248, 2021. doi.org/10.1016/j.chemolab.2021.104248
• D. Miksova, C. Rieser, and P. Filzmoser. Identification of mineralization in geochemistry along a transect based on the spatial curvature of log-ratios. Mathematical Geosciences, 53, 1513-1533, 2021. doi.org/10.1007/s11004-021-09930-4
• D. Miksova, C. Rieser, P. Filzmoser, M. Middleton, and R. Sutinen. Identification of mineralization in geochemistry for grid sampling using generalized additive models. Mathematical Geosciences, 53, 1861-1880, 2021. doi.org/10.1007/s11004-021-09929-x
• G.S. Monti and P. Filzmoser. Sparse least trimmed squares regression with compositional covariates for high dimensional data. Bioinformatics, 37(21), 3805-3814, 2021. doi.org/10.1093/bioinformatics/btab572
• L. Moreau, C. Draily, J.-M. Cordy, K. Boyle, M. Buckley, E. Gjesfjeld, P. Filzmoser, V. Borgia, S.A. Gibson, J. Day, R. Beyer, A. Manica, M. Vander Linden, M. de Grooth and S. Pirson. Adaptive trade-offs towards the Last Glacial Maximum in north-western Europe: a multidisciplinary view from Walou Cave. Journal of Paleolithic Archaeology, volume 4, 2021. doi.org/10.1007/s41982-021-00078-5
• N. Mumic and P. Filzmoser. A multivariate test for detecting fraud based on Benford's law, with application to music streaming data. Statistical Methods and Applications, 30, 819-840, 2021. doi.org/10.1007/s10260-021-00582-6
• T. Ortner, P. Filzmoser, M. Rohm, S. Brodinova, and C. Breiteneder. Local projections for high-dimensional outlier detection. Metron, 79:189-206, 2021. doi.org/10.1007/s40300-020-00183-5
• S. Perez-Fernandez, P. Martinez-Camblor, P. Filzmoser, and N. Corral. Visualizing the decision rules behind ROC curves: understanding the classification process. Advances in Statistical Analysis, 105(1), 135-161, 2021. doi.org/10.1007/s10182-020-00385-2
• J. Rabeder, H. Reitner, I. Wimmer-Frey, P. Filzmoser, M.C. Mert, M. Heinrich, P. Lipiarski, J.M. Reitner, G. Hobiger, and C. Benold. Integrative Analyse der Löss- und Lösslehmvorkommen im österreichischen Alpenvorland und im Wiener Becken - ein Beitrag zum Interaktiven Rohstoff-Informationssystem IRIS-Online. Berg- und Hüttenmännische Monatshefte, 166(4), 206-211, 2021. doi.org/10.1007/s00501-021-01096-0
• C. Rieser and P. Filzmoser. Compositional trend filtering. Annales Mathematicae et Informaticae, 53, 257-270, 2021. doi.org/10.33039/ami.2021.02.004
• C. Rieser and P. Filzmoser. Outlier detection for pandemic-related data using compositional functional data analysis. In: M. Boado-Penas, J. Eisenberg, and S. Sahin (Eds.), Pandemics: Insurance and Social Protection. Springer Nature, Cham, Switzerland, pp. 251-266, 2021. doi.org/10.1007/978-3-030-78334-1_12
• N. Stefelova, A. Alfons, J. Palarea-Albaladajo, P. Filzmoser, and K. Hron. Robust regression with compositional covariates including cellwise outliers. Advances in Data Analysis and Classification, 15, 869-909, 2021. doi.org/10.1007/s11634-021-00436-9
• K.G. van den Boogaart, P. Filzmoser, K. Hron, M. Templ, and R. Tolosana-Delgado. Classical and robust regression analysis with compositional data. Mathematical Geosciences, 53, 823-858, 2021. doi.org/10.1007/s11004-020-09895-w
• K. Varmuza, M. Dehmer, F. Emmert-Streib, and P. Filzmoser. Automorphism groups of alkane graphs. Croatica Chemica Acta, 94, 47-58, 2021. doi.org/10.5562/cca3807

2020

• S. Acitas, P. Filzmoser, and B. Senoglu. A new partial robust adaptive modified maximum likelihood estimator. Chemometrics and Intelligent Laboratory Systems.. To appear.
• S. Acitas, P. Filzmoser, and B. Senoglu. S. Acitas, P. Filzmoser, and B. Senoglu. A robust adaptive modified maximum likelihood estimator for the linear regression model. Journal of Statistical Computation and Simulation. To appear. doi.org/10.1080/00949655.2020.1856847
• S. de la Rosa de Saa, M.A. Lubiano, B. Sinova, P. Filzmoser, and M.A. Gil. Location-free robust scale estimates for fuzzy data. IEEE Transactions on Fuzzy Systems. To appear. doi.org/10.1109/TFUZZ.2020.2984203
• M. Drastichova and P. Filzmoser. The relationship between health outcomes and health expenditure in Europe by using compositional data analysis. Problemy Ekorozwoju -- Problems of Sustainable Development. To appear.
• P. Filzmoser and M. Gregorich. Multivariate outlier detection in applied data analysis: global, local, compositional and cellwise outliers. Mathematical Geosciences, 52(8), 1049-1066, 2020. doi.org/10.1007/s11004-020-09861-6
• P. Filzmoser, S. Höppner, I. Ortner, S. Serneels, and T. Verdonck. Cellwise robust M regression. Computational Statistics & Data Analysis. To appear. doi.org/10.1016/j.csda.2020.106944
• P. Filzmoser and K. Hron. Compositional data analysis in chemometrics. In S. Brown, R. Tauler, B. Walczak, editors, Comprehensive Chemometrics: Chemical and Biochemical Data Analysis, Second Edition, Elsevier, Amsterdam, pp. 641-662, 2020.
• P. Filzmoser and K. Nordhausen. Robust linear regression for high-dimensional data: an overview. WIREs Computational Statistics. To appear.
• P. Filzmoser, S. Serneels, R. Maronna, and C. Croux. Robust multivariate methods in chemometrics. In S. Brown, R. Tauler, B. Walczak, editors, Comprehensive Chemometrics: Chemical and Biochemical Data Analysis, Second Edition, Elsevier, Amsterdam, pp. 393-430, 2020.
• K. Hron, M. Engle, P. Filzmoser, and E. Fiserova. Weighted symmetric pivot coordinates for compositional data with geochemical applications. Mathematical Geosciences. To appear. doi.org/10.1007/s11004-020-09862-5
• B. Lemiere, J. Melleton, V. Derycke, E. Gloaguen, L. Bouat, D. Miksova, P.Filzmoser, and M. Middleton. pXRF measurements on soil samples for the exploration of an antimony deposit: example from the Vendean antimony district (France). Minerals, Vol.10, Issue 8, 2020. doi.org/10.3390/min10080724.
• D. Miksova, P. Filzmoser, and M. Middleton. Imputation of values above an upper detection limit in compositional data. Computers and Geosciences. To appear. doi.org/10.1016/j.cageo.2019.104383
• D. Miksova, C. Rieser, P. Filzmoser, S.M. Thaarup, and J. Melleton. A method to identify geochemical mineralization on linear transects. Austrian Journal of Statistics. To appear.
• G.S. Monti and P. Filzmoser. High-dimensional regression with compositional covariates: a robust perspective. In A. Pollice, N. Salvati, and F. Schirripa Spagnolo: Book of Short Papers SIS 2020. Pearson, pp. 105-110, 2020.
• K. Nordhausen, P. Filzmoser, and G. Fischer. Blind source separation for compositional time series. Mathematical Geosciences. To appear.
• I. Ortner, P. Filzmoser, and C. Croux. Robust and sparse multigroup classification by the optimal scoring approach. Data Mining and Knowledge Discovery, 34, 723-741, 2020. doi.org/10.1007/s10618-019-00666-8
• D. Rosadi, E. Putranda Setiawan, M. Templ, and P. Filzmoser. Robust covariance estimators for mean-variance portfolio optimization with transaction lots. Operations Research Perspectives. To appear.
• M. Templ, J. Gussenbauer, and P. Filzmoser. Evaluation of robust outlier detection methods for zero-inflated complex data. Journal of Applied Statistics, 47(7), 1144-1167, 2020.
• K. Varmuza, P. Filzmoser, N. Fray, H. Cottin, S. Merouane, O. Stenzel, J. Kissel, C. Briois, D. Baklouti, A. Bardyn, S. Siljeström, J. Silen, and M. Hilchenbach. Composition of cometary particles collected during two periods of the Rosetta mission - multivariate evaluation of mass spectral data. Journal of Chemometrics. To appear. doi.org/10.1002/cem.3218
• J. Walach, P. Filzmoser, S. Kouril, D. Friedecky, and T. Adam. Cellwise outlier detection and biomarker identification in metabolomics based on pairwise log-ratios. Journal of Chemometrics, 34:e3182, 2020. doi.org/10.1002/cem.3182

2019

• S. Brodinova, P. Filzmoser, T. Ortner, C. Breiteneder, and M. Rohm. Robust and sparse k-means clustering for high-dimensional data. Advances in Data Analysis and Classification. To appear.
• M. Drastichova and P. Filzmoser. Assessment of sustainable development using cluster analysis and principal component analysis. Problemy Ekorozwoju -- Problems of Sustainable Development, 14(2), 7-24, 2019.
• P. Filzmoser and K. Hron. Comments on: Compositional data: the sample space and its structure. Test, 28(3), 639-643, 2019.
• C. Gozzi, P. Filzmoser, A. Buccianti, O. Vaselli, and B. Nisi. Statistical methods for the geochemical characterisation of surface waters: The case study of the Tiber River basin (Central Italy). Computers and Geosciences, 131, 80--88, 2019.
• L. Moreau, A. Ciornei, E. Gjesfjeld, P. Filzmoser, S.A. Gibson, J. Day, P.R. Nigst, P. Noiret, R.A. Macleod, L. Nita, and M. Anghelinu. First geochemical 'fingerprinting' of Balkan and Prut flint from Palaeolithic Romania: potentials, limitations and future directions. Archaeometry, 61(3), 521-538.
• D. Rosadi and P. Filzmoser. Robust second-order least-squares estimation for regression models with autoregressive errors. Statistical Papers, 50(1), 105-122, 2019.
• A. Wurl, A. Falkner, P. Filzmoser, A. Haselböck, A. Mazak, and S. Sperl. A comprehensive prediction approach for hardware asset management. In: C. Quix, J. Bernardino (eds): Data Management Technologies and Applications. DATA 2018. Communications in Computer and Information Science, vol 862. Springer, Cham, 2019. To appear.
• A. Wurl, A. Falkner, A. Haselböck, A. Mazak, and P. Filzmoser. Exploring robustness in a combined feature selection approach. Proceedings of the 8th International Conference on Data Science, Technology and Applications -- Volume 1: DATA, pages 84-91, Prague, Czech Republic, 2019.

2018

• C.H. Abreu-Junior, M.J. de Lima Brossi, R.T. Monteiro, P.H.S. Cardoso, T. da Silva Mandu, T.A. Rodrigues Nogueirab, A. Ganga, P. Filzmoser, F. Carvalho de Oliveira, L. Pittol Firmee, Z. Hef, G.F. Capra. Effects of sewage sludge application on unfertile tropical soils evaluated by multiple approaches: a field experiment in a commercial Eucalyptus plantation. Science of the Total Environment, 655, 1457-1467, 2018.
• M. Brandl, M.M. Martinez, C. Hauzenberger, P. Filzmoser, P. Nymoen, and N. Mehler. A multi-technique analytical approach to sourcing Scandinavian flint: Provenance of ballast flint from the shipwreck "Leirvigen 1", Norway. PLoS ONE, 13(8): e0200647. Online version
• G.F. Capra, S. Tidu, R. Lovreglio, G. Certini, M. Salis, V. Bacciu, A. Ganga, and P. Filzmoser. The impact of large fire on calcareous Mediterranean pedosystems (Sardinia, Italy) - An integrated multiple approach. Science of the Total Environment, 624, 1152-1162, 2018.
• M.A. Di Palma, P. Filzmoser, M. Gallo, and K. Hron. A robust Parafac model for compositional data. Journal of Applied Statistics, 45(8), 1347-1369, 2018.
• A.P. Duarte Silva, P. Filzmoser, and P. Brito. Outlier detection in interval data. Advances in Data Analysis and Classification, 12(3), 785-822, 2018.
• P. Filzmoser and F.S. Kurnaz. A robust Liu regression estimator. Communications in Statistics - Simulation and Computation, 47(2), 432-443, 2018.
• B. Flem, C. Reimann, K. Fabian, M. Birke, P. Filzmoser, and D. Banks. Graphical statistics to explore the natural and anthropogenic processes influencing the inorganic quality of drinking water, ground water and surface water. Applied Geochemistry, 88(Part B), 133-148, 2018.
• F.S. Kurnaz, I. Hoffmann, and P. Filzmoser. Robust and sparse estimation methods for high dimensional linear and logistic regression. Chemometrics and Intelligent Laboratory Systems, 172, 211-222, 2018. PDF-File
• M. Landauer, M. Wurzenberger, F. Skopik, G. Settanni, and P. Filzmoser. Dynamic log file analysis: An unsupervised cluster evolution approach for anomaly detection. Computers & Security, 79, 94-116, 2018.
• M. Landauer, M. Wurzenberger, F. Skopik, G. Settanni, and P. Filzmoser. Time series analysis: Unsupervised anomaly detection beyond outlier detection. In: C. Su and H. Kikuchi (eds.) Information Security Practice and Experience. ISPEC 2018. Lecture Notes in Computer Science, vol 11125. Springer, Cham. pp. 19-36, 2018.
• M.C. Mert, P. Filzmoser, G. Endel, and I. Wilbacher. Compositional data analysis in epidemiology. Statistical Methods in Medical Research, 27(6), 1878-1891, 2018.
• G.S. Monti, P. Filzmoser, and R.C. Deutsch. A robust approach to risk assessment based on species sensitivity distributions. Risk Analysis: An International Journal, 38 (10), 2073-2086, 2018.
• L. Moreau, A. Ciornei, E. Gjesfjeld, P. Filzmoser, S.A. Gibson, J. Day, P.R. Nigst, P. Noiret, R.A. Macleod, L. Nita, and M. Anghelinu. First geochemical 'fingerprinting' of Balkan and Prut flint from Palaeolithic Romania: potentials, limitations and future directions. Archaeometry. To appear.
• T. Ortner, P. Filzmoser, M. Zaharieva, C. Breiteneder, and S. Brodinova. Guided projections for analysing the structure of high-dimensional data. Journal of Computational and Graphical Statistics, 27(4), 750-762, 2018.
• S. Perez-Fernandez, P. Martinez-Camblor, P. Filzmoser, and N. Corral. nsROC: An R package for non-standard ROC curve analysis. The R Journal, 2018. PDF-File
• C. Reimann, P. Englmaier, B. Flem, O.A. Eggen, T.E. Finne, M. Anderson, and P. Filzmoser. The response of 12 different plant materials and one mushroom to Mo and Pb mineralisation along a 100-km transect in southern central Norway. Geochemistry: Exploration, Environment, Analysis, 18(3): 204-215, 2018.
• C. Reimann, K. Fabian, M. Birke, P. Filzmoser, A. Demetriades, P. Negrel, K. Oorts, J. Matschullat, P. de Caritat, and the GEMAS Project Team. GEMAS: Establishing geochemical background and threshold for 53 chemical elements in European agricultural soil. Applied Geochemistry, 88(B), 302-318, 2018.
• C. Reimann, K. Fabian, B. Flem, M. Anderson, P. Filzmoser, and P. Englmaier. Geosphere-biosphere circulation of chemical elements in soil and plant systems from a 100 km transect from southern central Norway. Science of the Total Environment, 639, 129-145, 2018.
• K. Varmuza, P. Filzmoser, I. Hoffmann, J. Walach, H. Cottin, N. Fray, C. Briois, P. Modica, A. Bardyn, J. Silen, S. Siljeström, O. Stenzel, J. Kissel, and M. Hilchenbach. Significance of variables for discrimination - applied to the search of organic ions in mass spectra measured on cometary particles. Journal of Chemometrics, 32(4), 1-13, DOI 10.1002/cem.3001, 2018.
• O. Vencalek, K. Hron, and P. Filzmoser. A comparison of generalized linear models and compositional data analysis for parameter estimation in ordered categories models. Statistical Modelling. To appear.
• J. Walach, P. Filzmoser, and K. Hron. Data normalization and scaling: Consequences for the analysis in omics sciences. In: J. Jaumot, C. Bedia, and R. Tauler (eds.) Comprehensive Analytical Chemistry. Data Analysis for Omics Sciences: Methods and Applications. Elsevier, Amsterdam, The Netherlands, pp. 165-196, 2018.
• A. Zimek and P. Filzmoser. There and back again: Outlier detection between statistical reasoning and data mining algorithms. WIREs Data Mining and Knowledge Discovery. 8: null. doi: 10.1002/widm.1280. online access

2017

• A. Alfons, C. Croux, and P. Filzmoser. Robust maximum association estimators. Journal of the American Statistical Association, 112 (517), 436-445, 2017.
• M. Bögl, P. Filzmoser, T. Gschwandtner, T. Lammarsch, R. Leite, S. Miksch, and A. Rind. Cycle plot revisited: multivariate outlier detection using a distance-based abstraction. Computer Graphics Forum, 36, 227-238, 2017.
• S. Brodinova, M. Zaharieva, P. Filzmoser, T. Ortner, and C. Breiteneder. Clustering of imbalanced high-dimensional media data. Advances in Data Analysis and Classification, 12(2), 261-284, 2017.
• S. de la Rosa de Saa, M.A. Lubiano, B. Sinova, and P. Filzmoser. Robust scale estimators for fuzzy data. Advances in Data Analysis and Classification, 11, 731-758, 2017.
• P. Filzmoser and Yu. Kharin (eds.). Austrian Journal of Statistics, 46(3 & 4), 2017.
• L. Grad-Gyenge and P. Filzmoser. The paradigm of relatedness. In W. Abramowicz, R. Alt, and B. Franczyk, editors, Business Information Systems Workshops. BIS 2016. Lecture Notes in Business Information Processing, Springer, Cham, vol. 263, pp. 57-68, 2017.
• K. Hron, P. Brito, and P. Filzmoser. Exploratory data analysis for interval compositional data. Advances in Data Analysis and Classification, 11(2), 223-241, 2017.
• K. Hron, P. Filzmoser, P. de Caritat, E. Fiserova, and A. Gardlo. Weighted pivot coordinates for compositional data and their application to geochemical mapping. Mathematical Geosciences, 49, 797-814, 2017.
• P. Kynclova, K. Hron, and P. Filzmoser. Correlation between compositional parts based on symmetric balances. Mathematical Geosciences, 49(6), 777-796, 2017.
• C. Reimann, P. Filzmoser, K. Hron, P. Kynclova, and R.G. Garrett. A new method for correlation analysis of compositional (environmental) data - a worked example. Science of the Total Environment, 607-608, 965--971, 2017.
• C. Reimann, P. Negrel, A. Ladenberger, M. Birke, P. Filzmoser, P. O'Connor, and A. Demetriades. Comment on "Maps of heavy metals in the soils of the European Union and proposed priority areas for detailed assessment" by G. Toth, T. Hermann, G. Szatmari, and L. Pasztor. Science of the Total Environment, 578, 236-241, 2017.
• O.J. Stenzel, M. Hilchenbach, S. Merouane, J. Paquette, K. Varmuza, C. Engrand, F. Brandstaetter, C. Koeberl, L. Ferriere, P. Filzmoser, S. Siljestroem, and the COSIMA team. Similarities in element content between Comet 67P/Churyumov-Gerasimenko coma dust and selected meteorite samples. Mothly Notices of the Royal Astronomical Society, 469, Supp. 2, 492-505, 2017.
• B. Templ, M. Templ, P. Filzmoser, A. Lehoczky, B. Czucz, E. Baksiene, S. Fleck, H. Gregow, S. Hodzic, G. Kalvane, E. Kubin, P. Vello, D. Romanovskaja, V. Vucetic, A. Zust, and Team NS-Pheno. Phenological patterns of flowering across biogeographical regions of Europe. International Journal of Biometeorology, 61, 1347-1358, 2017.
• M. Templ, K. Hron, and P. Filzmoser. Exploratory tools for outlier detection in compositional data with structural zeros. Journal of Applied Statistics, 44(4), 734-752, 2017.
• J. Tobin, J. Walach, D. de Beer, P.J. Williams, P. Filzmoser, and B. Walczak. Untargeted anlysis of chromatographic data for green and fermented rooibos: problem with size effect removal. Journal of Chromatography A, 1525, 109-115, 2017.
• J. Walach, P. Filzmoser, K. Hron, B. Walczak, and L. Najdekr. Robust biomarker identification in a two-class problem based on pairwise log-ratios. Chemometrics and Intelligent Laboratory Systems, 171, 277-285, 2017.

2016

• C. Agostinelli, A. Basu, P. Filzmoser, and D. Mukherjee (eds.). Recent advances in robust statistics: theory and applications. Springer India, New Delhi, 201 pp., 2016. (ISBN: 978-81-322-3641-2)
  
• S. Aivazian, P. Filzmoser, and Yu. Kharin (eds.). Computer Data Analysis and Modeling: Complex Stochastic Data and Systems. Proceedings of the Eleventh International Conference, ISBN 978-985-553-366-6. Belarusian State University, Minsk, 2016.
  
• A. Alfons, C. Croux, and P. Filzmoser. Robust maximum association between data sets: The R package ccaPP. Austrian Journal of Statistics, 45(1), 71-79. PDF-File
  
• G.F. Capra, A. Ganga, P. Filzmoser, C. Gaviano, and S. Vacca. Combining local and scientific knowledge on soil resources through an integrated ethnopedological approach. CATENA - An Interdisciplinary Journal of Soil Science - Hydrology - Geomorphology focusing on Geoecology and Landscape Evolution, 142, 89-101.
  
• P. Filzmoser, K. Hron, and R. Tolosana-Delgado (eds.). Special Section on Geochemical Statistics, Applied Geochemistry, Vol. 75, 2016, with Preface on pp. 169-170.
  
• L. Grad-Gyenge and P. Filzmoser. Recommendation techniques on a knowledge graph for email remarketing. The Eighth International Conference on Information, Process, and Knowledge Management ThinkMind, Venice, Italy. To appear.
  
• I. Hoffmann, P. Filzmoser, S. Serneels, and K. Varmuza. Sparse and robust PLS for binary classification. Journal of Chemometrics, 30, 153-162. PDF-File
  
• K. Hron, A. Menafoglio, M. Templ, K. Hruzova, and P. Filzmoser. Simplicial principal component analysis for density functions in Bayes spaces. Computational Statistics and Data Analysis, 94, 330-350.
  
• K. Hruzova, V. Todorov, K. Hron, and P. Filzmoser. Classical and robust orthogonal regression between parts of compositional data. Statistics. To appear. PDF-File
  
• P. Kynclova, P. Filzmoser, and K. Hron. Compositional biplots including external non-compositional variables. Statistics, 50(5), 1132-1148. PDF-File
  
• J.M. McKinley, K. Hron, E. Grunsky, C. Reimann, P. de Caritat, P. Filzmoser, K.G. van den Boogaart, and R. Tolosana-Delgado. The single component geochemical map: fact or fiction. Journal of Geochemical Exploration, 162, 16-28.
• M.C. Mert, P. Filzmoser, and K. Hron. Error propagation in isometric logratio coordinates for compositional data: theoretical and practical considerations. Mathematical Geosciences, 48(8), 941-961.
• L. Moreau, M. Brandl, P. Filzmoser, C. Hauzenberger, E. Goemaere, I. Jadin, H. Collet, A. Hauzeur, and R.W. Schmitz. Geochemical sourcing of flint artifacts from western Belgium and the German Rhineland: testing hypotheses on Gravettian period mobility and raw material economy. Geoarchaeology: An International Journal, 31(3), 229-243.
• C. Reimann, P. Negrel, A. Ladenberger, M. Birke, P. Filzmoser, P. O'Connor, and A. Demetriades. Comment on "Heavy metals in agricultural soil of the European Union with implications for food safety" by G. Toth, T. Hermann, M.R. Da Silva, and L. Montanarella. Environment International, 97, 258-263.
• M. Templ. K. Hron, P. Filzmoser, and A. Gardlo. Imputation of rounded zeros for high-dimensional compositional data. Chemometrics and Intelligent Laboratory Systems, 155, 183-190.

2015

• M. Bögl, W. Aigner, P. Filzmoser, T. Gschwandtner, T. Lammarsch, S. Miksch, and A. Rind. Integrating predictions in time series model selection. Proceedings of the EuroVis Workshop on Visual Analytic, EuroVA, Cagliari, Italy, EuroGraphics, pp. 73-77, 2015.
• M. Bögl, W. Aigner, P. Filzmoser, T. Gschwandtner, T. Lammarsch, S. Miksch, and A. Rind. Visually and statistically guided imputation of missing values in univariate seasonal time series. Poster Proceedings of the IEEE Visualization Conference 2015, Chicago, USA, 2015. (Best Poster Award)
• S. de la Rosa de Saa, P. Filzmoser, M.A. Gil, and M.A. Lubiano. On the robustness of absolute deviations with fuzzy data. In P. Grzegorzewski, M. Gagolewski, O. Hryniewicz, and M.A. Gil, editors, Strengthening Links Between Data Analysis and Soft Computing, Springer-Verlag, Heidelberg, 2015, pages 133-141.
  
• P. Filzmoser and K. Hron. Robust coordinates for compositional data using weighted balances. In K. Nordhausen and S. Taskinen, editors,, Modern Nonparametric, Robust and Multivariate Methods. Festschrift in Honour of Hannu Oja, Springer, Berlin, 2015, pages 167-184.
  
• P. Filzmoser and K. Hron. Special issue: Compositional data modelling. Statistical Modelling, Vol. 15(2), pp. vii-viii.
  
• B. Flem, C. Reimann, M. Birke, D. Banks, P. Filzmoser, and B. Frengstad. Inorganic chemical quality of European tap-water: 2. Geographical distribution. Applied Geochemistry, 59, 211-224.
  
• L. Grad-Gyenge, P. Filzmoser, and H. Werthner. Recommendations on a knowledge graph. In MLRec 2015: 1st International Workshop on Machine Learning Methods for Recommender Systems, 2015, pages 13-20.
  
• I. Hoffmann, S. Serneels, P. Filzmoser, and C. Croux. Sparse partial robust M regression. Chemometrics and Intelligent Laboratory Systems, 149, 50-59. PDF-File
  
• K. Hron and P. Filzmoser. Exploring compositional data with the robust compositional biplot. In M. Carpita, E. Brentari, and E.M. Qannari, editors,, Advances in Latent Variables. Part of the series Studies in Theoretical and Applied Statistics, Springer International Publishing, 2015, pages 219-226.
  
• A. Kalivodova, K. Hron, P. Filzmoser, L. Najdekr, H. Janeckova, and T. Adam. PLS-DA for compositional data with application to metabolomics. Journal of Chemometrics, 29(1), 21-28.
• P. Kynclova, P. Filzmoser, and K. Hron. Modeling compositional time series with vector autoregressive models. Journal of Forecasting, 34, 303-314.
• J.-A. Martin-Fernandez, K. Hron, M. Templ, P. Filzmoser, and J. Palarea-Albaladejo. Bayesian-multiplicative treatment of count zeros in compositional data sets. Statistical Modelling, 15(2),134-158.
• M.C. Mert, P. Filzmoser, and K. Hron. Sparse principal balances. Statistical Modelling, 15(2), 159-174.
• K. Nordhausen, H. Oja, P. Filzmoser, and C. Reimann. Blind source separation for spatial compositional data. Mathematical Geosciences, 47(7), 753-770.
• T. Ortner, P. Filzmoser, and G. Endel. Identifying structural changes in Austrian social insurance data. IFAC-PapersOnLine, 48-1, 115-120.
• K. Varmuza and P. Filzmoser. Repeated double cross validation (rdCV) -- a strategy for optimizing empirical multivariate models, and for comparing their prediction performances. In M. Khanmohammadi, editor, Current Applications of Chemometrics, Nova Science Publishers, New York, USA, 2015, pages 15-31.
  

2014

• M. Brandl, C. Hauzenberger, W. Postl, M.M. Martinez, P. Filzmoser, and G. Trnka. Radiolarite studies at Krems-Wachtberg (Lower Austria): Northern Alpine vs. Carpathian lithic resources. Quaternary International, 351, 146-162.
  
• A. Demetriades, C. Reimann, and P. Filzmoser. Evaluation of GEMAS project quality control results. In C. Reimann, M. Birke, A. Demetriades, P. Filzmoser, and P. O'Connor, editors, Chemistry of Europe's Agricultural Soils. Part A: Methodology and Interpretation of the GEMAS Data Set, Geologisches Jahrbuch, Reihe B, Heft 102, Schweizerbart, Stuttgart, 2014, pages 47-60.
  
• P. Filzmoser, C. Gatu, and A. Zeileis. Special issue on statistical algorithms and software in R. Computational Statistics and Data Analysis, Vol. 71, pp. 887-888.
  
• P. Filzmoser and Yu. Kharin (eds.). Austrian Journal of Statistics, 43(3 & 4), 2014.
  
• P. Filzmoser and C. Reimann. Multivariate data analysis. In C. Reimann, M. Birke, A. Demetriades, P. Filzmoser, and P. O'Connor, editors, Chemistry of Europe's Agricultural Soils. Part A: Methodology and Interpretation of the GEMAS Data Set, Geologisches Jahrbuch, Reihe B, Heft 102, Schweizerbart, Stuttgart, 2014, pages 83-92.
  
• P. Filzmoser, C. Reimann, and M. Birke. Univariate data analysis and mapping. In C. Reimann, M. Birke, A. Demetriades, P. Filzmoser, and P. O'Connor, editors, Chemistry of Europe's Agricultural Soils. Part A: Methodology and Interpretation of the GEMAS Data Set, Geologisches Jahrbuch, Reihe B, Heft 102, Schweizerbart, Stuttgart, 2014, pages 67-81.
  
• P. Filzmoser, A. Ruiz-Gazen, and C. Thomas-Agnan. Identification of local multivariate outliers. Statistical Papers, 55(1), 29-47. PDF-File
  
• P. Filzmoser and B. Walczak. What can go wrong at the data normalization step for identification of biomarkers? Journal of Chromatography A, 1362, 194-205.
  
• K. Hron, P. Filzmoser, M. Templ, K.G. van den Boogaart, and R. Tolosana-Delgado. Robust regression with compositional response: application to geosciences. In G.-A. Carolina et al., editors, Mathematics of Planet Earth, Springer, Berlin, 2014, pages 87-90.
• W. Huf, K. Kalcher, R.N. Boubela, G. Rath, A. Vecsei, P. Filzmoser, and E. Moser. On the generalizability of resting-state fMRI machine learning classifiers. Frontiers in Human Neuroscience, 8 (July), 502. PDF-File
  
• K. Kalcher, R.N. Boubela, W. Huf, L. Bartova, C. Kronnerwetter, B. Derntl, L. Pezawas, P. Filzmoser, C. Nasel, and E. Moser. The spectral diversity of resting-state fluctuations in the human brain. PLOS ONE, PDF-File 9(4), e93375.
  
• A. Ladenberger, J. Uhlbäck, M. Andersson, C. Reimann, T. Tarvainen, G. Morris, M. Sadeghi, M. Eklund, and P. Filzmoser. Elemental patterns in agricultural and grazing land soil in Norway, Finland and Sweden: What have we learned from continental scale mapping. In C. Reimann, M. Birke, A. Demetriades, P. Filzmoser, and P. O'Connor, editors, Chemistry of Europe's Agricultural Soils. Part A: Methodology and Interpretation of the GEMAS Data Set, Geologisches Jahrbuch, Reihe B, Heft 103, Schweizerbart, Stuttgart, 2014, pages 236-251.
  
• N. Neykov, P. Filzmoser, and P. Neytchev. Ultrahigh dimensional variable selection through the penalized maximum trimmed likelihood estimator. Statistical Papers, 55(1), 187-207.
  
• C. Reimann, A. Demetriades, M. Birke, P. O'Connor, J. Halamic, A. Ladenberger, and The GEMAS Project Team. Distribution of elements/parameters in agricultural and grazing land soils of Europe. In C. Reimann, M. Birke, A. Demetriades, P. Filzmoser, and P. O'Connor, editors, Chemistry of Europe's Agricultural Soils. Part A: Methodology and Interpretation of the GEMAS Data Set, Geologisches Jahrbuch, Reihe B, Heft 102, Schweizerbart, Stuttgart, 2014, pages 103-474.
  
• C. Reimann, M. Birke, A. Demetriades, P. Filzmoser, and P. O'Connor (eds.). Chemistry of Europe's agricultural soils - Part A: Methodology and interpretation of the GEMAS data set. Geologisches Jahrbuch (Reihe B). Schweizerbarth, Hannover, 528 pp. (ISBN: 978-3-510-96846-6)
  
• C. Reimann, M. Birke, A. Demetriades, P. Filzmoser, and P. O'Connor (eds.). Chemistry of Europe's agricultural soils - Part B: General background information and further analysis of the GEMAS data set. Geologisches Jahrbuch (Reihe B). Schweizerbarth, Hannover, 352 pp. (ISBN: 978-3-510-96847-3)
  
• M. Templ and P. Filzmoser. Simulation and quality of a synthetic close-to-reality employer-employee population. Journal of Applied Statistics, 45(5), 1053-1072.
  
• V. Todorov and P. Filzmoser. Software tools for robust analysis of high-dimensional data. Austrian Journal of Statistics, 43(3-4), 255-266. PDF-File
  
• K. Varmuza, P. Filzmoser, M. Hilchenbach, H. Krüger, and J. Silen. KNN classification -- evaluated by repeated double cross validation: Recognition of minerals relevant for comet dust. Chemometrics and Intelligent Laboratory Systems, 138, 64-71.
  

2013

• A. Alfons, M. Templ, and P. Filzmoser. Robust estimation of economic indicators from survey samples based on Pareto tail modeling. Journal of the Royal Statistical Society, Series C, 62(2), 271-286, 2013. PDF-File
  
• D. Ballabio, M. Vasighi, and P. Filzmoser. Effects of supervised Self Organising Maps parameters on classification performance. Analytica Chimica Acta, 765, 45-53, 2013.
  
• M. Bögl, W. Aigner, P. Filzmoser, T. Lammarsch, S. Miksch, and A. Rind. Visual analytics for model selection in time series analysis. IEEE Transactions on Visualization and Computer Graphics, Special Issue "VIS 2013" 19(12), 2237-2246, 2013. PDF-File
  
• R.N. Boubela, K. Kalcher, W. Huf, C. Kronnerwetter, P. Filzmoser, and E. Moser. Beyond noise: Using temporal ICA to extract meaningful information from high-frequency fMRI signal fluctuations during rest. Frontiers in Human Neuroscience, 7, 1-12, 2013.
  
• C. Croux, P. Filzmoser, and H. Fritz. Robust sparse principal component analysis. Technometrics, 55(2), 202-214, 2013. PDF-File
  
• P. Filzmoser and K. Hron. Robustness for compositional data. In C. Becker, R. Fried and S. Kuhnt, editors, Robustness and Complex Data Structures, Festschrift in Honour of Ursula Gather, pp. 117-131, Springer Verlag, Heidelberg, 2013.
  
• P. Filzmoser and V. Todorov. Robust tools for the imperfect world. Information Sciences, 245, 4-20. PDF-File
  
• M. Gschwandtner and P. Filzmoser. Outlier detection in high dimension using regularization. Advances in Intelligent Systems and Computing, 190 AISC, 237-244, 2013.
  
• K. Hron and P. Filzmoser. Robust diagnostics of fuzzy clustering results using the compositional approach. Advances in Intelligent Systems and Computing, 190 AISC, 245-253, 2013.
  
• K. Hron, P. Filzmoser, S. Donevska, and E. Fiserova. Covariance-based variable selection for compositional data. Mathematical Geosciences, 45(4), 487-498, 2013. PDF-File
  
• K. Hron, M. Templ, and P. Filzmoser. Estimation of a proportion in survey sampling using the logratio approach. Metrika, 76(6), 799-818, 2013. PDF-File
  
• K. Kalcher, R.N. Boubela, W. Huf, B. Biswal, P. Baldinger, U. Sailer, P. Filzmoser, S. Kasper, C. Lamm, R. Lanzenberger, and E. Moser. RESCALE: Voxel-specific Task-fMRI Scaling Using Resting State Fluctuation Amplitude. NeuroImage, 70, 80-88, 2013.
  
• J.C. Martinez Avila, P. Filzmoser, and N.M. Neykov. Statistical modeling of hunting success using hunter surveys. Austrian Journal of Statistics, 42(2), 67-80, 2013. PDF-File
  
• M. Templ, S. Aklan, P. Filzmoser, M. Preusser, J.A. Hainfellner. Statistical indicators for the analysis of digitalized brain tumor images. Austrian Journal of Statistics, 42(2), 81-100, 2013. PDF-File
  
• R. Todeschini, D. Ballabio, V. Consonni, F. Sahigara, and P. Filzmoser. Locally-centred Mahalanobis distance: a new distance measure with salient features towards outlier detection. Analytica Chimica Acta, 787, 1-9, 2013.
• V. Todorov and P. Filzmoser. Comparing classical and robust sparse PCA. Advances in Intelligent Systems and Computing, 190 AISC, 283-291, 2013.
• K. Varmuza, P. Filzmoser, and M. Dehmer. Multivariate linear QSPR/QSAR models and their evaluation. Computational and Structural Biotechnology Journal, 5 (6), e201302007, 1-10, 2013. PDF-File

2012

• R.N. Boubela, W. Huf, K. Kalcher, R. Stadky, P. Filzmoser, L. Pezawas, S. Kasper, C. Windischberger, and E. Moser. A highly parallelized framework for computationally intensive MR data analysis. Magnetic Resonance Materials in Physics, Biology and Medicine (MAGMA), Vol. 25 (4), pp. 313-320, 2012.
  
• P. Filzmoser, M. Gschwandtner, and V. Todorov. Review of sparse methods in regression and classification with application to chemometrics. Journal of Chemometrics, Vol. 26, pp. 42-51, 2012. PDF-File
  
• P. Filzmoser, K. Hron, and C. Reimann. Interpretation of multivariate outliers for compositional data. Computers & Geosciences, Vol. 39, pp. 77-85, 2012. PDF-File
  
• P. Filzmoser, K. Hron, and M. Templ. Discriminant analysis for compositional data and robust parameter estimation. Computational Statistics, Vol. 27(4), pp. 585-604, 2012. PDF-File
  
• H. Fritz, P. Filzmoser, and C. Croux. A comparison of algorithms for the multivariate L1-median. Computational Statistics, Vol. 27, pp. 393-410, 2012. PDF-File
  
• K. Hron, P. Filzmoser, and K. Thompson. Linear regression with compositional explanatory variables. Journal of Applied Statistics, Vol. 39(5), pp.1115-1128, 2012. PDF-File
  
• K. Hron, M. Jelinkova, P. Filzmoser, R. Kreuziger, P. Bednar, P. Bartak. Statistical analysis of wines using a robust compositional biplot. Talanta, Vol. 90, pp. 46-50, 2012.
  
• K. Kalcher, W. Huf, R.N. Boubela, P. Filzmoser, L. Pezawas, B.B. Biswal, S. Kasper, E. Moser, and C. Windischberger. Fully exploratory network independent component analysis of the 1000 functional connectomes database. Frontiers in Human Neuroscience, Vol. 6, pp. 1-34, 2012.
  
• J.A. Martin-Fernandez, K. Hron, M. Templ, P. Filzmoser, and J. Palarea-Albaladejo. Model-based replacement of rounded zeros in compositional data: classical and robust approaches. Computational Statistics and Data Analysis, Vol. 56, pp. 2688-2704. PDF-File
  
• N.M. Neykov, P. Cizek, P. Filzmoser, and P.N. Neytchev. The least trimmed quantile regression. Computational Statistics and Data Analysis. Vol. 56(6), pp. 1757-1770, 2012. PDF-File
  
• N. Neykov, P. Filzmoser, and P. Neytchev. Robust joint modeling of mean and dispersion through trimming. Computational Statistics & Data Analysis, Vol. 56, pp. 34-48, 2012. PDF-File
  
• C. Pascoal, M.R. de Oliveira, R. Valadas, P. Filzmoser, P. Salvador, and A. Pacheco. Robust feature selection and robust PCA for internet traffic anomaly detection. Proceedings of the 31th IEEE International Conference on Computer Communications (IEEE INFOCOM 2012), Orlando, Florida, ISSN 0743-166X, pp. 1755-1763, 2012.
  
• C. Reimann, M. Birke, and P. Filzmoser. Temperature-dependent leaching of chemical elements from mineral water bottle materials. Applied Geochemistry, Vol. 27, pp. 1492-1498, 2012.
  
• C. Reimann, P. Filzmoser, K. Fabian, K. Hron, M. Birke, A. Demtriades, E. Dinelli, A. Ladenberger, and The GEMAS Project Team. The concept of compositional data analysis in practice - Total major element concentrations in agricultural and grazing land soils of Europe. Science of the Total Environment, Vol. 426, pp. 196-210, 2012.
  
• J. Sucharova, I. Suchara, M. Hola, S. Marikova, C. Reimann, R. Boyd, P. Filzmoser, and P. Englmaier. Top-/bottom-soil ratios and enrichment factors: What do they really show? Applied Geochemistry, Vol. 27, pp. 138-145, 2012.
  
• M. Templ, A. Alfons, and P. Filzmoser. Exploring incomplete data using visualization techniques. Advances in Data Analysis and Classification, Vol. 6(1), pp. 29-47, 2012. PDF-File
  
• K. Varmuza, P. Filzmoser, B. Liebmann, and M. Dehmer. Redundancy analysis for characterizing the correlation between groups of variables - applied to molecular descriptors. Chemometrics and Intelligent Laboratory Systems. Vol. 117, pp. 31-41, 2012.

2011

• A. Alfons, W.E. Baaske, P. Filzmoser, W. Mader, and R. Wieser. Robust variable selection with application to quality of life research. Statistical Methods and Applications, Vol. 20, pp. 65-82, 2011. PDF-File
  
• A. Alfons, S. Kraft, M. Templ, and P. Filzmoser. Simulation of close-to-reality population data for household surveys with application to EU-SILC. Statistical Methods and Applications, Vol. 20(3), pp. 383-407, 2011. PDF-File
  
• W. Berger, H. Piringer, P. Filzmoser, and E. Gröller. Unvertainty-aware exploration of continuous parameter spaces using multivariate prediction. Computer Graphics Forum, Vol. 30(3), pp. 911-920, 2011. (Best Paper Award at EuroVis 2011). PDF-File
  
• J.J. Egozcue, J. Daunis-i-Estadella, V. Pawlowsky-Glahn, K. Hron and P. Filzmoser. Simplicial regression. The normal model. Journal of Applied Probability and Statistics, Vol. 6, pp. 87-108, 2011. PDF-File
  
• P. Filzmoser and K. Hron. Robust statistical analysis. In V. Pawlowsky-Glahn and A. Buccianti, editors, Compositional Data Analysis. Theory and Applications, pp. 59-72, John Wiley & Sons, Chichester (UK), 2011.
  
• P. Filzmoser and Yu. Kharin (eds.). Austrian Journal of Statistics, 40(1 & 2), 2011.
  
• P. Filzmoser and V. Todorov. Review of robust multivariate statistical methods in high dimension. Analytica Chimica Acta, Vol. 705, pp. 2-14, 2011. PDF-File
  
• W. Huf, K. Kalcher, G. Pail, M.-E. Friedrich, P. Filzmoser, and S. Kasper. Meta-analysis: Fact or fiction? How to interpret Meta-analyses. The World Journal of Biological Psychiatry, Vol. 12, pp. 188-200, 2011.
  M. Templ, K. Hron, and P. Filzmoser. robCompositions: an R-package for robust statistical analysis of compositional data. In V. Pawlowsky-Glahn and A. Buccianti, editors, Compositional Data Analysis. Theory and Applications, pp. 341-355, John Wiley & Sons, Chichester (UK), 2011.
  
• M. Templ, A. Kowarik, and P. Filzmoser. Iterative stepwise regression imputation using standard and robust methods. Computational Statistics and Data Analysis. Vol. 55, pp. 2793-2806, 2011. PDF-File
  
• A. Sarbu, G.A. Janauer, U. Schmidt-Mumm, P. Filzmoser, D. Smarandache, and G. Pascale. Characterization of the potamal Danube River and the Delta: connectivity determines indicative macrophyte assemblages. Hydrobiologia, Vol. 671, pp. 75-93, 2011.
  
• J. Sucharova, I. Suchara, M. Hola, C. Reimann, R. Boyd, P. Filzmoser, and P. Englmaier. Linking chemical elements in forest floor humus in the Czech Republic to contamination sources. Environmental Pollution, Vol. 159, pp. 1205-1214, 2011.
  
• J. Sucharova, I. Suchara, M. Hola, C. Reimann, R. Boyd, P. Filzmoser, and P. Englmaier. The performance of moss, grass, and 1- and 2-year old spruce needles as bioindicators of contamination: A comparative study at the scale of the Czech Republic. Science of the Total Environment, Vol. 409, pp. 2281-2297, 2011.
  
• J. Sucharova, I. Suchara, M. Hola, C. Reimann, R. Boyd, P. Filzmoser, and P. Englmaier. Spatial distribution of lead and lead isotopes in soil B-horizon, forest-floor humus, grass (Avenella flexuosa) and spruce (Picea abies) needles across the Czech Republic. Applied Geochemistry, Vol. 26, pp. 1205-1214, 2011.
  
• V. Todorov, M. Templ, and P. Filzmoser. Detection of multivariate outliers in business survey data with incomplete information. Advances in Data Analysis and Classification (ADAC). Vol. 5(1), pp. 37--56, 2011. PDF-File
  
• H. Treiblmaier and P. Filzmoser. Benefits from using continuous rating scales in online survey research. Proceedings of the International Conference on Information Systems (ICIS), Shanghai, China, December 4-7, 2011. Association for Information Systems 2011, ISBN 978-0-615-55907-0. Article
  
• C. Turkay, P. Filzmoser, and H. Hauser. Brushing dimensions--A dual visual analysis model for high-dimensional data. IEEE Transactions on Visualization and Computer Graphics, Vol. 17(12), pp. 2591--2599, 2011.
  
• K. Varmuza, C. Engrand, P. Filzmoser, M. Hilchenbach, J. Kissel, H. Krüger, J. Silen, and M. Trieloff. Random projection for dimensionality reduction - Applied to time-of-flight secondary ion mass spectrometry data. Analytica Chimica Acta, Vol. 705, pp. 48-55, 2011.
  

2010

• S. Aivazian, P. Filzmoser, and Yu. Kharin (eds.). Computer Data Analysis and Modeling: Complex Stochastic Data and Systems. Proceedings of the Ninth International Conference, Volume 1, ISBN 978-985-476-847-2. Belarusian State University, Minsk, 2010.
  
• S. Aivazian, P. Filzmoser, and Yu. Kharin (eds.). Computer Data Analysis and Modeling: Complex Stochastic Data and Systems. Proceedings of the Ninth International Conference, Volume 2, ISBN 978-985-476-848-9. Belarusian State University, Minsk, 2010.
  
• A. Alfons, M. Templ, and P. Filzmoser An object-oriented framework for statistical simulation: the R package simFrame. Journal of Statistical Software, Vol. 37(3), pp. 1-36, 2010. PDF-File
  
• A. Alfons, M. Templ, and P. Filzmoser Contamination models in the R package simFrame for statistical simulation. In S. Aivazian, P. Filzmoser, and Yu. Kharin, editors, Proceedings of the Ninth International Conference on Computer Data Analysis and Modeling, volume 2, pp. 178-181, Belarusian State University, Minsk, 2010. PDF-File
  
• A. Alfons, M. Templ, P. Filzmoser, and J. Holzer. A comparison of robust methods for Pareto tail modeling in the case of Laeken indicators. In C. Borgelt, G. Gonzalez-Rodriguez, W. Trutschnig, M.A. Lubiano, M.A. Gil, P. Grzegorzewski, and O. Hryniewicz, editors, Combining Soft Computing and Statistical Methods in Data Analysis, pp. 17-24, Springer-Verlag, Berlin, 2010. PDF-File
  
• K. Böheim, S.-M. Pok, M. Schlögel, and P. Filzmoser. Active middle ear implant compared with open-fit hearing aid in sloping high-frequency sensorineural hearing loss. Otology & Neurotolgy. Vol. 31(3), pp. 424-429, 2010.
  
• P. Filzmoser. Soft methods in robust statistics. In C. Borgelt, G. Gonzalez-Rodriguez, W. Trutschnig, M.A. Lubiano, M.A. Gil, P. Grzegorzewski, and O. Hryniewicz, editors, Combining Soft Computing and Statistical Methods in Data Analysis, pp. 273-280, Springer-Verlag, Berlin, 2010. PDF-File
  
• P. Filzmoser and K. Hron Multivariate outlier detection with compositional data. In S. Aivazian, P. Filzmoser, and Yu. Kharin, editors, Proceedings of the Ninth International Conference on Computer Data Analysis and Modeling, volume 1, pp. 45-52, Belarusian State University, Minsk, 2010. PDF-File
  
• P. Filzmoser and K. Hron. Robust multivariate methods for compositional data. In Y. Lechevallier and G. Saporta, editors, COMPSTAT 2010 - Proceedings in Computational Statistics, pp. 79-88, Springer-Verlag, Berlin, 2010. PDF-File
  
• P. Filzmoser, K. Hron, and C. Reimann. The bivariate statistical analysis of environmental (compositional) data. Science of the Total Environment, Vol. 408, pp. 4230-4238, 2010. PDF-File
  
• K. Hron and P. Filzmoser. Elements of robust regression for data with absolute and relative information. In C. Borgelt, G. Gonzalez-Rodriguez, W. Trutschnig, M.A. Lubiano, M.A. Gil, P. Grzegorzewski, and O. Hryniewicz, editors, Combining Soft Computing and Statistical Methods in Data Analysis, pp. 329-335, Springer-Verlag, Berlin, 2010. PDF-File
  
• K. Hron, M. Templ, and P. Filzmoser Exploratory compositional data analysis using the R-package robCompositions. In S. Aivazian, P. Filzmoser, and Yu. Kharin, editors, Proceedings of the Ninth International Conference on Computer Data Analysis and Modeling, volume 1, pp. 179-186, Belarusian State University, Minsk, 2010. PDF-File
  
• K. Hron, M. Templ, and P. Filzmoser. Imputation of missing values for compositional data using classical and robust methods. Computational Statistics and Data Analysis, Vol. 54, pp. 3095-3107, 2010. PDF-File
  
• Z. Karacsony and P. Filzmoser. Asymptotic normality of kernel type regression estimators for random fields. Journal of Statistical Planning and Inference, Vol. 140, pp. 872-886, 2010. PDF-File
  
• J. Kehrer, P. Filzmoser, and H. Hauser. Brushing moments in interactive visual analysis. Computer Graphics Forum, Vol. 29(3), pp. 813-822, 2010. PDF-File
  
• B. Liebmann, P. Filzmoser, and K. Varmuza. Robust and classical PLS regression compared. Journal of Chemometrics, Vol. 24, pp. 111-120, 2010. PDF-File
  
• C. Reimann, M. Birke, and P. Filzmoser. Bottled drinking water: water contamination from bottle materials (glass, soft PET, hard PET), the influence of colour and acidification. Applied Geochemistry. Vol. 25, pp. 1030-1046, 2010. PDF-File
  
• C. Reimann, M. Birke, and P. Filzmoser. Reply to the comment "Bottled drinking water: water contamination from bottle materials (glass, hard PET, soft PET), the influence of colour and acidification" by Hayo Müller-Simon. Applied Geochemistry. Vol. 25, pp. 1464-1465, 2010. PDF-File
  
• V. Todorov and P. Filzmoser. Robust statistic for the one-way MANOVA. Computational Statistics and Data Analysis, Vol. 54, pp. 37-48, 2010. PDF-File
  
• H. Treiblmaier and P. Filzmoser. Exploratory factor analysis revisited: How robust methods support the detection of hidden multivariate data structures in IS research. Information and Management, Vol. 47, 197-207, 2010.
  
• K. Varmuza, P. Filzmoser, and B. Liebmann. Random projection experiments with chemometric data. Journal of Chemometrics, Vol. 24, pp. 209-217, 2010. PDF-File
  

2009

• W.E. Baaske, P. Filzmoser, W. Mader, and R. Wieser. Agriculture as a success factor for municipalities. H. Peyerl, editor, Jahrbuch der Österreichischen Gesellschaft für Agrarökonomie Vol. 18(1), pp. 21-30, Facultas Verlag, Wien, 2009. PDF-File
  
• P. Filzmoser. Discussion of "Invariant co-ordinate selection," by D.E. Tyler, F. Critchley, L. Dümbgen, H. Oja. Journal of the Royal Statistical Society B, Vol. 71(3), p. 583, 2009.
  
• P. Filzmoser, K. Hron. Correlation analysis for compositional data. Mathematical Geosciences, Vol. 41, pp. 905-919, 2009. PDF-File
  
• P. Filzmoser, K. Hron, and C. Reimann. Univariate statistical analysis of environmental (compositional) data: Problems and possibilities. Science of the Total Environment, Vol. 407, pp. 6100-6108, 2009. PDF-File
  
• P. Filzmoser, K. Hron, and C. Reimann. Principal component analysis for compositional data with outliers. Environmetrics, PDF-File Vol. 20, pp. 621-632, 2009.
  
• P. Filzmoser, K. Hron, C. Reimann, and R.G. Garrett. Robust factor analysis for compositional data. Computers and Geosciences, Vol. 35, pp. 1854-1861, 2009. PDF-File
  
• P. Filzmoser, B. Liebmann, and K. Varmuza. Repeated double cross validation. Journal of Chemometrics, Vol. 23(4), pp. 160-171, 2009. PDF-File
  
• P. Filzmoser, S. Serneels, R. Maronna, and P.J. Van Espen. Robust multivariate methods in chemometrics. In B. Walczak, R.T. Ferre, and S. Brown, editors, Comprehensive Chemometrics, 2009, pp. 681-722. PDF-File
  
• C. Reimann, A. Demetriades, O.A. Eggen, P. Filzmoser and the EuroGeoSurveys Geochemistry expert group. The EuroGeoSurveys geochemical mapping of agricultural and grazing land soils project (GEMAS) - Evaluation of quality control results of aqua regia extraction analysis. Technical Report 2009.049, Geological Survey of Norway (NGU), Trondheim, Norway, 2009. PDF-File
  
• V. Todorov and P. Filzmoser. An object oriented framework for robust multivariate analysis. Journal of Statistical Software, Vol. 32(3), pp. 1-47, 2009. PDF-File
  

2008

• C. Croux, P. Filzmoser, and K. Joossens. Classification efficiencies for robust linear discriminant analysis. Statistica Sinica, Vol. 18, pp. 581-599, 2008. PDF-File
  
• R. Dutter, P. Filzmoser, and Yu. Kharin (eds.). Austrian Journal of Statistics, 37(1), 2008.
  
• P. Filzmoser and K. Hron. Outlier detection for compositional data using robust methods. Mathematical Geosciences, Vol. 40(3), pp. 233-248, 2008. PDF-File
  
• P. Filzmoser, R. Maronna, and M. Werner. Outlier identification in high dimensions. Computational Statistics and Data Analysis, Vol. 52, pp. 1694-1711, 2008. PDF-File
  
• M. Templ, P. Filzmoser, and C. Reimann. Cluster analysis applied to regional geochemical data: Problems and possibilities. Applied Geochemistry, Vol. 23(8), pp. 2198-2213, 2008.
  
• A. Weisser, G. Endel, P. Filzmoser, and M. Gyimesi. ATC -> ICD - evaluating the reliability of prognoses for ICD-10 diagnoses derived from the ATC-Code of prescriptions. BMC Health Services Research, Vol. 8(Suppl I):A10, 2008. PDF-File
  

2007

• S. Aivazian, P. Filzmoser, and Yu. Kharin (eds.). Computer Data Analysis and Modeling: Complex Stochastic Data and Systems. Proceedings of the Eighth International Conference, Volume 1, ISBN 978-985-476-508-2. Belarusian State University, Minsk, 2007.
  
• S. Aivazian, P. Filzmoser, and Yu. Kharin (eds.). Computer Data Analysis and Modeling: Complex Stochastic Data and Systems. Proceedings of the Eighth International Conference, Volume 2, ISBN 978-985-476-505-1. Belarusian State University, Minsk, 2007.
  
• C. Croux and P. Filzmoser. Discussion of "A Survey of Robust Statistics," by S. Morgenthaler. Statistical Methods and Applications, Vol. 15(3), pp. 271-293, 2007. PDF-File
  
• C. Croux, P. Filzmoser, and M.R. Oliveira. Algorithms for projection-pursuit robust principal component analysis. Chemometrics and Intelligent Laboratory Systems, Vol. 87, pp. 218-225, 2007. PDF-File
  
• P. Filzmoser and H. Fritz Exploring high-dimensional data with robust principal components. In S. Aivazian, P. Filzmoser, and Yu. Kharin, editors, Proceedings of the Eighth International Conference on Computer Data Analysis and Modeling, volume 1, pp. 43-50, Belarusian State University, Minsk, 2007. PDF-File
  
• N. Neykov, P. Filzmoser, R. Dimova, and P. Neytchev. Robust fitting of mixtures using the trimmed likelihood estimator. Computational Statistics and Data Analysis, Vol. 17(3), pp. 299-308, 2007. PDF-File
  
• C. Reimann, A. Arnoldussen, P. Englmaier, P. Filzmoser, T.E. Finne, R.G. Garrett, F. Koller, and Ø. Nordgulen. Element concentrations and variations along a 120-km transect in southern Norway - Anthropogenic vs. geogenic vs. biogenic element sources and cycles. Applied Geochemistry, Vol. 22, pp. 851-871, 2007. PDF-File
  

2006

• I. Fazekas and P. Filzmoser. A functional central limit theorem for kernel type density estimators. Austrian Journal of Statistics, Vol. 35(4), pp. 409-418, 2006. PDF-File
  
• P. Filzmoser, K. Joossens, and C. Croux. Multiple group linear discriminant analysis: Robustness and error rate. In A. Rizzi and M. Vichi, editors, COMPSTAT 2006 - Proceedings in Computational Statistics, pp. 521-532, Physica-Verlag, Heidelberg, 2006. PDF-File
  
• P. Filzmoser, S. Serneels, C. Croux, and P.J. Van Espen. Robust multivariate methods: The projection pursuit approach. In M. Spiliopoulou, R. Kruse, C. Borgelt, A. Nürnberger, and W. Gaul, editors, From Data and Information Analysis to Knowledge Engineering, pp. 270-277, Springer Verlag, Berlin, 2006. PDF-File
  
• G.A. Janauer, P. Filzmoser, H. Otahelova, A. Gaberscik, J. Topic, A. Berczik, R. Igic, V. Vulchev, A. Sarbu, A. Kohler, and N. Exler. Macrophyte Habitat Preference, River Restoration, and the WFD: making use of the MIDCC data base. In Proceedings 36th International Conference of IAD, pp. 81-85, Austrian Committee Danube Research/IAD, Vienna. ISBN 13:978-3-9500723-2-7, 2006. PDF-File
  
• G.A. Janauer, E. Lanz, P. Filzmoser, and N. Exler. Breg and Brigach, source streams of the Danube: changes based on macrophyte surveys 1967, 1989, and 2004. In Proceedings 36th International Conference of IAD, pp. 86-90, Austrian Committee Danube Research/IAD, Vienna. ISBN 13:978-3-9500723-2-7, 2006. PDF-File
  
• A. Sarbu, G. Janauer, N. Exler, and P. Filzmoser. The aquatic vegetation of large Danube river branches in Romania. In Proceedings 36th International Conference of IAD, pp. 101-106, Austrian Committee Danube Research/IAD, Vienna. ISBN 13:978-3-9500723-2-7, 2006. PDF-File
  
• B. Richter, M. Gwechenberger, P. Filzmoser, M. Marx, P. Lercher, and H.D. Gössi nger. Is inducibility of atrial fibrillation after radio frequency ablation really a relevant prognostic factor? European Heart Journal, Vol. 27, pp. 2553-2559, 2006. html-File
  
• S. Serneels, C. Croux, P. Filzmoser, and P.J. Van Espen. The partial robust M approach. In M. Spiliopoulou, R. Kruse, C. Borgelt, A. Nürnberger, and W. Gaul, editors, From Data and Information Analysis to Knowledge Engineering, pp. 230-237, Springer Verlag, Berlin, 2006. PDF-File
  
• D. Zick, H. Gassner, P. Filzmoser, J. Wanzenböck, B. Pamminger-Lahnsteiner, and G. Tischler. Changes in the fish species composition of all Austrian lakes >50 ha during the last 150 years. Fisheries Management and Ecology, Vol. 13, pp. 103-111, 2006. PDF-File
  Article in Science for Environment Policy from the European Commission: PDF-File

2005

• J.A. Branco, C. Croux, P. Filzmoser, and M.R. Oliveira. Robust canonical correlations: a comparative study. Computational Statistics, Vol. 20(2), pp. 203-229, 2005. PDF-File
  
• R. Dutter, P. Filzmoser, and Yu. Kharin (eds.). Austrian Journal of Statistics, 34(2), 2005.
  
• P. Filzmoser. Identification of multivariate outliers: a performance study. Austrian Journal of Statistics, Vol. 34(2), pp. 127-138, 2005. PDF-File
  
• P. Filzmoser, C. Reimann, and R.G. Garrett. Multivariate outlier detection in exploration geochemistry. Computers and Geosciences, Vol. 31, pp. 579-587, 2005. PDF-File
  
• P.J. van Helvoort, P. Filzmoser, and P.F.M. van Gaans. Sequential factor analysis as a new approach to multivariate analysis of heterogeneous geochemical datasets: an application to a bulk chemical characterization of fluvial deposits (Rhine-Meuse delta, The Netherlands). Applied Geochemistry, Vol. 20, pp. 2233-2251, 2005. PDF-File
  
• C. Reimann, P. Filzmoser, and R.G. Garrett. Background and threshold: critical comparison of methods of determination. Science of the Total Environment, Vol. 346, pp. 1-16, 2005. PDF-File
  
• S. Serneels, C. Croux, P. Filzmoser, and P.J. Van Espen. Partial robust M-regression. Chemometrics and Intelligent Laboratory Systems, Vol. 79(1-2), pp. 55-64, 2005. PDF-File
  
• S. Serneels, P. Filzmoser, C. Croux, and P.J. Van Espen. Robust continuum regression. Chemometrics and Intelligent Laboratory Systems, Vol. 76(2), pp. 197-204, 2005. PDF-File
  

2004

• S. Aivazian, P. Filzmoser, and Yu. Kharin (eds.). Computer Data Analysis and Modeling: Robustness and Computer Intensive Methods. Proceedings of the Seventh International Conference, Volume 1, ISBN 985-445-492-4. Belarusian State University, Minsk, 2004.
  
• S. Aivazian, P. Filzmoser, and Yu. Kharin (eds.). Computer Data Analysis and Modeling: Robustness and Computer Intensive Methods. Proceedings of the Seventh International Conference, Volume 2, ISBN 985-445-492-4. Belarusian State University, Minsk, 2004.
  
• P. Filzmoser. A multivariate outlier detection method. In S. Aivazian, P. Filzmoser, and Yu. Kharin, editors, Proceedings of the Seventh International Conference on Computer Data Analysis and Modeling, volume 1, pp. 18-22, Belarusian State University, Minsk, 2004. PDF-File
  
• P. Filzmoser and R. Viertl. Testing hypotheses with fuzzy data: The fuzzy p-value. Metrika, Vol. 59, No. 1, pp. 21-29, 2004. PDF-File
  
• N. Neykov, P. Filzmoser, R. Dimova, and P. Neytchev. Mixture of GLMs and the trimmed likelihood methodology. In J. Antoch, editor, COMPSTAT, Proceedings in Computational Statistics, pp. 1585-1592, Physica-Verlag, Heidelberg, 2004. PDF-File PS-File
  
• M.R. Oliveira, J.A. Branco, C. Croux, and P. Filzmoser. Robust redundancy analysis by alternating regression. In M. Hubert, G. Pison, A. Struyf and S. Van Aelst, editors, Theory and Applications of Recent Robust Methods, Series: Statistics for Industry and Technology, pp. 235-246, Birkhauser, Basel, 2004. PDF-File
  

2003

• C. Croux, P. Filzmoser, G. Pison, and P.J. Rousseeuw. Fitting multiplicative models by robust alternating regressions. Statistics and Computing, Vol. 13, pp. 23-36, 2003. PDF-File PS-File
  
• R. Dutter, P. Filzmoser, U. Gather, and P.J. Rousseeuw (eds.). Developments in Robust Statistics. International Conference on Robust Statistics 2001. Physica-Verlag, Heidelberg, 2003.
  
• P. Filzmoser and C. Croux. Dimension reduction of the explanatory variables in multiple linear regression. Pliska Stud. Math. Bulgar., Vol. 14, pp. 59-70, 2003. PDF-File PS-File
  
• P. Filzmoser, G.A. Janauer, and N. Exler. A statistical method for finding indicators of water quality. In M. Ruoppa, P. Heinonen, A. Pilke, S. Rekolainen, H. Toivonen, and H. Vuoristo, editors, How to Assess and Monitor Ecological Quality in Freshwaters, pp. 19-23, Nordic Council of Ministers, Copenhagen, 2003.
  
• G. Pison, P.J. Rousseeuw, P. Filzmoser, and C. Croux. Robust factor analysis. Journal of Multivariate Analysis, Vol. 84, pp. 145-172, 2003. PDF-File
  

2002

• R. Dutter, P. Filzmoser, U. Gather, and P.J. Rousseeuw (eds.). Metrika, 55(1), 2002.
  
• R. Dutter, P. Filzmoser, U. Gather, and P.J. Rousseeuw (eds.). Metrika, 55(2), 2002.
  P. Filzmoser. Robust factor analysis: methods and applications. In G.A. Marcoulides and I. Moustaki, editors, Latent Variable and Latent Structure Models, pp. 153-194, Lawrence Erlbaum Associates, Mahwah (New Jersey), 2002.
  
• P. Filzmoser and C. Croux. A projection algorithm for regression with collinearity. In K. Jajuga, A. Sokolowski, and H.-H. Bock, editors, Classification, Clustering, and Data Analysis, pp. 227-234, Springer-Verlag, Berlin, 2002. PDF-File
  
• P. Filzmoser and C. Reimann. Robust multivariate methods in geostatistics. In W. Gaul and G. Ritter, editors, Classification, Automation, and New Media, pp. 429-436, Springer-Verlag, Berlin, 2002. PDF-File
  
• P. Filzmoser and P.J. Rousseeuw. Robust statistics. The Encyclopedia of Life Support Systems. EOLSS Publishers, Oxford, UK. http://www.eolss.net
  
• C. Reimann, P. Filzmoser, and R.G. Garrett. Factor analysis applied to regional geochemical data: problems and possibilities. Applied Geochemistry. Vol. 17, pp. 185-206, 2002. PDF-File
  

2001

• P. Filzmoser Robust principal component regression. In S. Aivazian, Y. Kharin, and H. Rieder, editors, Proceedings of the Sixth International Conference on Computer Data Analysis and Modeling, Vol. 1, pp. 132-137, Minsk, Belarus, 2001. PDF-File PS-File
  
• C. Reimann, H. Niskavaara, G. Kashulina, P. Filzmoser, R. Boyd, T. Volden, O. Tomilina, and I. Bogatyrev. Critical remarks on the use of terrestrial moss (Hylocomium splendens and Pleurozium schreberi) for monitoring of airborne pollution. Environmental Pollution, Vol. 113(1), pp. 41-57, 2001. PDF-File
  
• P. Filzmoser. Orthogonal principal planes. Psychometrika, Vol. 65(3), pp. 363-376, 2000. PDF-File PS-File
  
• P. Filzmoser, C. Dehon and C. Croux. Outlier resistant estimators for canonical correlation analysis. In J.G. Betlehem and P.G.M. van der Heijden, editors, COMPSTAT, Proceedings in Computational Statistics, pp. 301-306, Physica-Verlag, Heidelberg, 2000. PDF-File PS-File
  
• G. Pison, P.J. Rousseeuw, P. Filzmoser, and C. Croux. A robust version of principal factor analysis. In J.G. Betlehem and P.G.M. van der Heijden, editors, COMPSTAT, Proceedings in Computational Statistics, pp. 385-390, Physica-Verlag, Heidelberg, 2000. PDF-File PS-File
  
• C. Reimann and P. Filzmoser. Normal and lognormal data distribution in geochemistry: Death of a myth. Consequences for the statistical treatment of geochemical and environmental data. Environmental Geology, Vol. 39(9), pp. 1001-1014, 2000. PDF-File
  

1999

• P. Filzmoser. Robust principal component and factor analysis in the geostatistical treatment of environmental data. Environmetrics, Vol. 10, pp. 363-375, 1999 PS-File
  
• P. Filzmoser, R. Baumgartner, E. Moser. A hierarchical clustering method for analyzing functional MR images. Magnetic Resonance Imaging, Vol. 17, pp. 817-826, 1999. PDF-File
  
• P. Filzmoser, C. Croux, G. Pison and P.J. Rousseeuw. Robust estimation in the factor analysis model. In H. Friedl, A. Berghold, and G. Kauermann, editors, Proceedings of the 14th International Workshop on Statistical Modelling, pp. 513-515, Graz, Austria, 1999.
  

1998

• C. Croux and P. Filzmoser. Robust factorization of a data matrix. In R. Payne and P. Green, editors, COMPSTAT, Proceedings in Computational Statistics, pp. 245-249, Physica-Verlag, Heidelberg, 1998. PDF-File PS-File
  
• C. Croux and P. Filzmoser. A robust biplot representation of two-way tables. In A. Rizzi, M. Vichi, and H.-H. Bock, editors, Advances in Data Science and Classification, pp. 355-361, Springer-Verlag, Berlin, 1998. Zipped PS-File
  
• P. Filzmoser. The usage of robust multivariate methods in combination with geostatistical tools. In A. Buccianti, G. Nardi, and R. Potenza, editors, Proceedings of IAMG'98, pages 415-420, De Frede, Naples, Italy, 1998.
  
• P. Filzmoser. Generalized principal planes. In K. Fernandez-Aguirre and A. Morineau, editors, Analyses Multidimensionelles des Donnees, IV Congres International NGUS'97, pp. 87-100, Cisia-Ceresta, Saint-Mande, 1998. PDF-File PS-File
  
• P. Filzmoser, R. Baumgartner, and E. Moser. A split and merge algorithm for analyzing functional MR images. Magnetic Resonance Materials in Physics, Biology and Medicine (MAGMA), Vol. 6, Suppl. 1, pp. 90-91, 1998.
  

1997

• K. Böheim, A. Nahler, H. Schlögel und P. Filzmoser. Ergebnisse mit Goldpistons bei der Stapedektomie mit kleiner Fensterung. Otorhinolaryngol Nova, Vol. 7, pp. 235-240, 1997.
  
• P. Filzmoser. Finding structures of interest in a large data set using factor analysis. Austrian Journal of Statistics, Vol. 26(2), pp. 27-34, 1997. PDF-File PS-File
  
• P. Filzmoser. Investigation of the Language in Germany and Austria Using Statistical Methods. In R. Klar and O.Opitz, editors, Classification and Knowledge Organization, pages 658-660, Heidelberg, 1997. Springer-Verlag.
  
• C. Kollmann, P. Filzmoser, W. Backfrieder und H. Bergmann. Ein automatisiertes Verfahren zur Segmentierung der Farbinformation von amplitudenkodierten Ultraschall-Farbdopplergeräten (AKDS). In K. Beckh et al., editors, Ultraschall in der Medizin, Seite 79, Stuttgart, 1997. Georg Thieme Verlag.