A robust biplot

This paper introduces a robust biplot which is related to multivariate M-estimates. The n × p data matrix is first considered as a sample of size n from some p-variate population, and robust M-estimates of the population location vector and scatter matrix are calculated. In the construction of the biplot, each row of the data matrix is assigned a weight determined in the preliminary robust estimation. In a robust biplot, one can plot the variables in order to represent characteristics of the robust variance-covariance matrix: the length of the vector representing a variable is proportional to its robust standard deviation, while the cosine of the angle between two variables is approximately equal to their robust correlation. The proposed biplot also permits a meaningful representation of the variables in a robust principal-component analysis. The discrepancies between least-squares and robust biplots are illustrated in an example. Cet article propose un biplot robuste construit a l'aide de M-estimateurs multivaries. On considere d'abord la matrice nxp des donnees comme un echantillon de taille n d'une population ap variables et on calcule des M-estimations robustes du vecteur des parametres de position et de la matrice de dispersion theorique. Dans la construction du biplot, chaque ligne de la matrice des donnees recoit un poids determine dans l'estimation robuste preliminaire. Le biplot robuste peut ětre construit de telle sorte que le graphique des variables represente les composantes de la matrice de variances-covariances robuste: la longueur du vecteur representant une variable est proportionnelle a sa deviation standard robuste tandis que le cosinus de l'angle entre deux variables est egal au coefficient de correlation robuste entre ces deux variables. Le biplot propose permet en outre des representations des variables dans une analyse en composantes principales robuste. Un exemple permet de faire une comparaison du biplot des moindres carres avec le biplot robuste.

[1]  L.C.A. Corsten,et al.  Graphical exploration in comparing covariance matrices , 1976 .

[2]  J. Gower Some distance properties of latent root and vector methods used in multivariate analysis , 1966 .

[3]  N. Campbell Robust Procedures in Multivariate Analysis I: Robust Covariance Estimation , 1980 .

[4]  A. Morineau,et al.  Multivariate descriptive statistical analysis , 1984 .

[5]  Ian T. Jolliffe,et al.  Rotation of ill-defined principal components , 1989 .

[6]  David E. Tyler Some results on the existence, uniqueness, and computation of the M-estimates of multivariate location and scatter , 1988 .

[7]  F. Critchley Influence in principal components analysis , 1985 .

[8]  Werner A. Stahel,et al.  Robust Statistics: The Approach Based on Influence Functions , 1987 .

[9]  K. Gabriel,et al.  Least Squares Approximation of Matrices by Additive and Multiplicative Models , 1978 .

[10]  I. Jolliffe,et al.  Influential observations in principal component analysis:a case study , 1988 .

[11]  K. Gabriel,et al.  The biplot graphic display of matrices with application to principal component analysis , 1971 .

[12]  C. Eckart,et al.  The approximation of one matrix by another of lower rank , 1936 .

[13]  Louis-Paul Rivest,et al.  L'analyse en composantes principales robuste , 1988 .

[14]  R. Clarke,et al.  Theory and Applications of Correspondence Analysis , 1985 .

[15]  Frank A. Hanna Measuring the Activity of Small Manufacturers , 1966 .

[16]  R. Maronna Robust $M$-Estimators of Multivariate Location and Scatter , 1976 .

[17]  S. Zamir,et al.  Lower Rank Approximation of Matrices by Least Squares With Any Choice of Weights , 1979 .