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RESEARCH PRODUCT
Affine equivariant multivariate rank methods
Hannu OjaJyrki MöttönenSamuli VisuriEsa OllilaVisa Koivunensubject
Statistics and ProbabilityPure mathematicsApplied MathematicsMatrix t-distributionMultivariate normal distributionNormal-Wishart distributionCombinatoricsEstimation of covariance matricesScatter matrixStatistics::MethodologyMatrix normal distributionMultivariate t-distributionStatistics Probability and UncertaintyMathematicsMultivariate stable distributiondescription
The classical multivariate statistical methods (MANOVA, principal component analysis, multivariate multiple regression, canonical correlation, factor analysis, etc.) assume that the data come from a multivariate normal distribution and the derivations are based on the sample covariance matrix. The conventional sample covariance matrix and consequently the standard multivariate techniques based on it are, however, highly sensitive to outlying observations. In the paper a new, more robust and highly efficient, approach based on an affine equivariant rank covariance matrix is proposed and outlined. Affine equivariant multivariate rank concept is based on the multivariate Oja (Statist. Probab. Lett. 1 (1983) 327) median.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2003-06-01 | Journal of Statistical Planning and Inference |