- Robust Multivariate Methods in Geostatistics Peter Filzmoser1, Clemens Reimann2
- Imputation of missing values for compositional data using classical and robust methods
- Random projection experiments with chemometric data Kurt Varmuzaa
- Robust Methods for Canonical Correlation Catherine Dehon1
- Robust Multivariate Methods: The Projection Pursuit Approach
- Robust Methods for Canonical Correlation Catherine Dehon 1 , Peter Filzmoser 2 , and Christophe Croux 1
- Abstract. Statistical hypothesis testing is very important for finding decisions in practical problems. Usually, the underlying data are assumed to be precise
- Outlier Identification in High Dimensions Peter Filzmoser a, Ricardo Maronna b
- Changes in the fish species composition of all Austrian lakes >50 ha during the last 150 years
- Fitting Multiplicative Models by Robust Alternating Regressions
- ORTHOGONAL PRINCIPAL PLANES Peter Filzmoser
- Review of robust multivariate statistical methods in high dimension $
- Noname manuscript No. (will be inserted by the editor)
- Robust Factorization of a Data Matrix Christophe Croux 1 and Peter Filzmoser 2
- ORTHOGONAL PRINCIPAL PLANES Peter Filzmoser
- Robust multivariate methods in Chemometrics Peter Filzmoser1
- Iterative stepwise regression imputation using standard and robust methods $
- JSS Journal of Statistical Software November 2010, Volume 37, Issue 3. http://www.jstatsoft.org/
- A comparison of robust methods for Pareto tail modeling in the case of
- Soft Methods in Robust Statistics Peter Filzmoser1
- Multivariate outlier detection with compositional P. Filzmoser(1)
- Asymptotic normality of kernel type regression estimators for random fields
- Eurographics/ IEEE-VGTC Symposium on Visualization 2010 G. Melanon, T. Munzner, and D. Weiskopf
- Bottled drinking water: water contamination from bottle materials (glass, hard PET, soft PET), the influence of colour and acidification
- Author's personal copy Reply to the comment ``Bottled drinking water: Water contamination
- Correlation analysis for compositional data Peter Filzmoser1
- CLASSIFICATION EFFICIENCIES FOR ROBUST LINEAR DISCRIMINANT ANALYSIS
- BioMed Central Page 1 of 1
- EXPLORING HIGH-DIMENSIONAL DATA WITH ROBUST PRINCIPAL COMPONENTS
- Macrophyte Habitat Preference, River Restoration, and the WFD: making use of the MIDCC data base.
- The aquatic vegetation of large Danube river branches in Romania Anca Srbu1
- 1 The partial robust M-approach Sven Serneels1
- Computers & Geosciences 31 (2005) 579587 Multivariate outlier detection in exploration geochemistry$
- Sequential Factor Analysis as a new approach to multivariate analysis of heterogeneous geochemical datasets
- Background and threshold: critical comparison of methods of determination
- Partial Robust M-Regression Sven Serneels1
- Statistics for Industry and Technology, 235246 c 2004 Birkhauser Verlag Basel/Switzerland
- Pliska Stud. Math. Bulgar. 14 (2003), 5970 STUDIA MATHEMATICA
- Journal of Multivariate Analysis 84 (2003) 145172 Robust factor analysis$
- Robust Principal Component P. Filzmoser
- Critical remarks on the use of terrestrial moss (Hylocomium splendens and Pleurozium schreberi) for monitoring
- Research article 1001Environmental Geology 39 (9) July Springer-Verlag
- Robust Factorization of a Data Matrix Christophe Croux1
- Generalized Principal Planes Peter Filzmoser1
- A Projection Algorithm for Regression with Collinearity
- A Robust Version of Principal Factor Analysis G. Pison 1 , P. J. Rousseeuw 1 , P. Filzmoser 2 & C. Croux 3
- Outlier Resistant Estimators for Canonical Correlation Analysis
- Robust continuum regression Sven Serneels1
- Repeated double cross validation Peter Filzmosera
- Stat Methods Appl manuscript No. (will be inserted by the editor)
- Factor analysis applied to regional geochemical data: problems and possibilities
- Robust Statistic for the One-way MANOVA Valentin Todorov a
- Robust Methods for Compositional Data Peter Filzmoser1
- Breg and Brigach, source streams of the Danube: changes based on macrophyte surveys 1967, 1989, and 2004
- Robust Principal Component and Factor Analysis in the Geostatistical Treatment of Environmental Data
- Noname manuscript No. (will be inserted by the editor)
- This article was originally published in a journal published by Elsevier, and the attached copy is provided by Elsevier for the
- Generalized Principal Planes Peter Filzmoser 1
- Fitting Multiplicative Models by Robust Alternating Regressions
- COMPSTAT'2004 Symposium c Physica-Verlag/Springer 2004 MIXTURE OF GLMS AND THE TRIMMED
- NGU Report 2009.049 The EuroGeoSurveys geochemical mapping of
- Multiple Group Linear Discriminant Analysis: Robustness and Error Rate
- Contamination models in the R package simFrame for statistical simulation
- JSS Journal of Statistical Software October 2009, Volume 32, Issue 3. http://www.jstatsoft.org/
- Algorithms for Projection-Pursuit Robust Principal Component Analysis
- Finding Structures of Interest in a Large Data Set Using Factor Analysis
- Principal component analysis for compositional data with outliers
- The bivariate statistical analysis of environmental (compositional) data
- Robust factor analysis for compositional data Peter Filzmoser a,, Karel Hron b
- PII S0730-725X(99)00014-4 q Original Contribution
- Elements of Robust Regression for Data with Absolute and Relative
- Robust and Classical PLS Regression Compared Bettina Liebmanna
- Univariate statistical analysis of environmental (compositional) data: Problems and possibilities
- Pliska Stud. Math. Bulgar. 14 (2003), 59{70 STUDIA MATHEMATICA
- Robust Principal Component P. Filzmoser
- Exploratory compositional data analysis using the R-package robCompositions
- Finding Structures of Interest in a Large Data Set Using Factor Analysis
- www.elsevier.com/locate/csda Author's Accepted Manuscript
- AUSTRIAN JOURNAL OF STATISTICS Volume 34 (2005), Number 2, 127138
- COMPSTAT'2004 Symposium PhysicaVerlag/Springer MIXTURE OF GLMS AND THE TRIMMED
- A MULTIVARIATE OUTLIER DETECTION P. Filzmoser
- A Robust Version of Principal Factor Analysis , P. J. Rousseeuw1
- Stat Methods Appl manuscript No. (will be inserted by the editor)
- Outlier Detection for Compositional Data Using Robust Methods
- Simplicial regression. The normal model 1 Simplicial regression. The normal model
- Interpretation of multivariate outliers for compositional Peter Filzmosera
- Robust joint modeling of mean and dispersion through trimming N.M. Neykov,a
- Review of Sparse Methods in Regression and Classification with Application to Chemometrics
- The Least Trimmed Quantile Regression N.M. Neykov,a
- Adv Data Anal Classif manuscript No. (will be inserted by the editor)
- Eurographics / IEEE Symposium on Visualization 2011 (EuroVis 2011) H. Hauser, H. Pfister, and J. J. van Wijk
- November 22, 2011 17:12 Journal of Applied Statistics accepted Journal of Applied Statistics
- Model-based replacement of rounded zeros in compositional data: classical and robust approaches$
