Robust PCA for skewed data and its outlier map

DOI10.1016/j.csda.2008.05.027zbMath1453.62116OpenAlexW2048993342MaRDI QIDQ961420

Tim Verdonck, Mia Hubert, Peter J. Rousseeuw

Publication date: 30 March 2010

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2008.05.027

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Factor analysis and principal components; correspondence analysis (62H25) Robustness and adaptive procedures (parametric inference) (62F35)

Related Items

Asymmetric \(\nu\)-tube support vector regression, Robust multivariate association and dimension reduction using density divergences, Robust classification for skewed data, Robust M-estimation of multivariate GARCH models, Special issue on variable selection and robust procedures, Detecting influential observations in principal components and common principal components, Detecting influential observations in kernel PCA, Pointwise probability reinforcements for robust statistical inference, \(\ell\) major component detection and analysis (\(\ell^1\) MCDA): foundations in two dimensions, Automatic Transformation and Integration to Improve Visualization and Discovery of Latent Effects in Imaging Data, Unnamed Item, Discussion of: ``The power of monitoring: how to make the most of a contaminated multivariate sample

Uses Software

• LIBRA
• ROBPCA

Cites Work

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• High breakdown estimators for principal components: the projection-pursuit approach revis\-ited
• Fast cross-validation of high-breakdown resampling methods for PCA
• Principal component analysis for data containing outliers and missing elements
• An adjusted boxplot for skewed distributions
• Breakdown properties of location estimates based on halfspace depth and projected outlyingness
• Robust principal component analysis for functional data. (With comments)
• Least Median of Squares Regression
• Between-Groups Comparison of Principal Components
• Normal Inverse Gaussian Distributions and Stochastic Volatility Modelling
• Multivariate SPC Charts for Monitoring Batch Processes
• Some Theorems on Quadratic Forms Applied in the Study of Analysis of Variance Problems, I. Effect of Inequality of Variance in the One-Way Classification