6533b7d0fe1ef96bd125a2a5
RESEARCH PRODUCT
Sign and Rank Covariance Matrices: Statistical Properties and Application to Principal Components Analysis
Esa OllilaHannu OjaChristophe Crouxsubject
Covariance matrixbusiness.industrySparse PCAPattern recognitionCovarianceKernel principal component analysisCorrespondence analysisScatter matrixPrincipal component analysisApplied mathematicsArtificial intelligencebusinessCanonical correlationMathematicsdescription
In this paper, the estimation of covariance matrices based on multivariate sign and rank vectors is discussed. Equivariance and robustness properties of the sign and rank covariance matrices are described. We show their use for the principal components analysis (PCA) problem. Limiting efficiencies of the estimation procedures for PCA are compared.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2002-01-01 |