Robust probabilistic PCA with missing data and contribution analysis for outlier detection

DOI10.1016/j.csda.2009.03.014zbMath1453.62067OpenAlexW2126111600WikidataQ66622084 ScholiaQ66622084MaRDI QIDQ961842

Elaine B. Martin, Tao Chen, Gary A. Montague

Publication date: 1 April 2010

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

Full work available at URL: http://epubs.surrey.ac.uk/69209/2/tchen09-csda.pdf

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 (7)

A latent variable model for two-dimensional canonical correlation analysis and the variational inference ⋮ On robust probabilistic principal component analysis using multivariate t -distributions ⋮ Process monitoring using a generalized probabilistic linear latent variable model ⋮ Detecting influential observations in principal components and common principal components ⋮ Detecting influential observations in kernel PCA ⋮ \(\ell\) major component detection and analysis (\(\ell^1\) MCDA): foundations in two dimensions ⋮ Liquidity commonality does not imply liquidity resilience commonality: a functional characterisation for ultra-high frequency cross-sectional LOB data

Uses Software

• BayesDA
• ROBPCA

Cites Work

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• Probabilistic Principal Component Analysis
• Principal component analysis based on robust estimators of the covariance or correlation matrix: influence functions and efficiencies
• A robust principal component analysis
• Multivariate T-Distributions and Their Applications
• Robust Statistics

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