The use of a common location measure in the invariant coordinate selection and projection pursuit

DOI10.1016/j.jmva.2016.08.007zbMath1348.62184arXiv1501.07240OpenAlexW2460929823WikidataQ57949670 ScholiaQ57949670MaRDI QIDQ321924

Fatimah Alashwali, John T. Kent

Publication date: 14 October 2016

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1501.07240

zbMATH Keywords

cluster analysisprojection pursuitinvariant coordinate selectionlocation measuresmultivariate mixture modelrobust scatter matrices

Mathematics Subject Classification ID

Nonparametric robustness (62G35) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (8)

Numerical Considerations and a new implementation for invariant coordinate selection ⋮ The relationship between shape parameters and kurtosis in some relevant models ⋮ Kurtosis removal for data pre-processing ⋮ Some theoretical properties of two kurtosis matrices, with application to invariant coordinate selection ⋮ Geodesic projection of the von Mises-Fisher distribution for projection pursuit of directional data ⋮ ICS for Multivariate Outlier Detection with Application to Quality Control ⋮ On the usage of joint diagonalization in multivariate statistics ⋮ Large-sample properties of unsupervised estimation of the linear discriminant using projection pursuit

Uses Software

• R
• MASS (R)
• robustbase
• ICS
• rrcov

Cites Work

• Unnamed Item
• Invariant Co-Ordinate Selection
• Eigenvectors of a kurtosis matrix as interesting directions to reveal cluster structure
• The length of the shorth
• Constrained \(M\)-estimation for multivariate location and scatter
• Normal and Student's \(t\) distributions and their applications
• Statistical Analysis of Financial Data in S-Plus
• Convergence Behavior of the em algorithm for the multivariate t -distribution
• Cluster Identification Using Projections
• A Projection Pursuit Algorithm for Exploratory Data Analysis
• A curious likelihood identity for the multivariate t-distribution
• Robust Statistics
• Robust Estimates of Location: Survey and Advances

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