Registro:

Referencias:

• Alqallaf, F., Van Aelst, S., Yohai, V. J., and Zamar, R. H. (2007), Propagation of Outliers in Multivariate Data, unpublished manuscript, available at http://mate.dm.uba.ar/vyohai/Alqallaf-VanAelst- Yohai-Zamar.pdf; Bay, S.D., The UCI KDD Archive [http://kdd.ics.uci. edu] (1999) University of California Irvine, Dept, , of Information and Computer Science
• Campbell, N.A., Robust Procedures in Multivariate Analysis I: Robust Covariance Estimation (1980) Applied Statistics, 29, pp. 231-237
• Croux, C., Haesbroeck, G., Principal Component Analysis Based on Robust Estimators of the Covariance or Correlation Matrix: Influence Functions and Efficiencies (2000) Biometrika, 87, pp. 603-618
• Croux, C., Ruiz-Gazen, A., A Fast Algorithm for Robust Principal Components Based on Projection Pursuit (1996) Compstat: Proceedings in Computational Statistics, pp. 211-216. , ed. A. Prat, Heidelberg: Physica-Verlag, pp
• Croux, C., Ruiz-Gazen, A., High-Breakdown Estimators for Principal Components: The Projection-Pursuit Approach Revisited (2005) Journal of Multivariate Analysis, 95, pp. 206-226
• Croux, C., Filzmoser, P., Pison, G., Rousseeuw, P.J., Fitting Multiplicative Models by Robust Alternating Regressions (2003) Statistics and Computing, 13, pp. 23-36
• De la Torre, F., Black, M.J., Robust Principal Components Analysis for Computer Vision (2001) Proceedings of the International Conference on Computer Vision, , http://citeseer.ist.psu.edu/torre01robust.html, available at
• Devlin, S.J., Gnanadesikan, R., Kettenring, J.R., Robust Estimation of Dispersion Matrices and Principal Components (1981) Journal of the American Statistical Association, 76, pp. 354-362
• Gabriel, K.R., Zamir, S., Lower-Rank Approximation of Matrices by Least Squares With Any Choice of Weights (1979) Technometrics, 21, pp. 489-498
• Hubert, M., Rousseeuw, P.J., Vanden Branden, K., ROBPCA: A New Approach to Robust Principal Component Analysis (2005) Technometrics, 47, pp. 64-79
• Janssens, K., Deraedt, I., Freddy, A., Veekman, J., Composition of 15th-17th Century Archaeological Glass Vessels Excavated in Antwerp, Belgium (1998) Mikrochimica Acta, 15 (SUPPL.), pp. 253-267
• Li, G., Chen, Z., Projection-Pursuit Approach to Robust Dispersion Matrices and Principal Components: Primary Theory and Monte Carlo (1985) Journal of the American Statistical Association, 80, pp. 759-766
• Liu, L., Hawkins, D.M., Ghosh, S., Young, S.S., Robust Singular Value Decomposition Analysis of Microarray Data (2003) Proceedings of the National Academy of Sciences, 100, pp. 13167-13172
• Locantore, N., Marron, J.S., Simpson, D.G., Tripoli, N., Zhang, J.T., Cohen, K.L., Robust Principal Components for Functional Data (1999) Test, 8, pp. 1-28
• Maronna, R.A., Principal Components and Orthogonal Regression Based on Robust Scales (2005) Technometrics, 47, pp. 264-273
• Maronna, R. A., and Yohai, V. J. (2007), Robust Lower-Rank Approximation of Data Matrices With Elementwise Contamination, unpublished manuscript, available at http://mate.dm.uba.ar/̃vyohai/LowRankFull. pdf; Maronna, R.A., Zamar, R.H., Robust Estimation of Location and Dispersion for High-Dimensional Data Sets (2002) Technometrics, 44, pp. 307-317
• Maronna, R.A., Martin, R.D., Yohai, V.J., (2006) Robust Statistics: Theory and Methods, , New York: Wiley
• Naga, R., Antille, G., Stability of Robust and Non-Robust Principal Component Analysis (1990) Computational Statistics & Data Analysis, 10, pp. 169-174
• Rey, W. (2007), Total Singular Value Decomposition: Robust SVD, Regression and Location-Scale, unpublished manuscript, available at http://arxiv.org/abs/0706.0096; Serneels, S., Verdonck, T., Principal Component Analysis for Data Containing Outliers and Missing Elements (2008) Computational Statistics and Data Analysis, 52, pp. 1712-1727
• Sigillito, V.G., Wing, S.P., Hutton, L.V., Baker, K.B., Classification of Radar Returns From the Ionosphere Using Neural Networks (1989) Johns Hopkins APL Technical Digest, 10, pp. 262-266
• Verboon, P., Heiser, W.J., Resistant Lower-Rank Approximation of Matrices by Iterative Majorization (1994) Computational Statistics and Data Analysis, 18, pp. 457-467
• Yohai, V.J., High-Breakdown Point and High-Efficiency Estimates for Regression (1987) The Annals of Statistics, 15, pp. 642-656
• Yohai, V.J., Zamar, R.H., High-Breakdown Estimates of Regression by Means of the Minimization of an Efficient Scale (1988) Journal of the American Statistical Association, 83, pp. 406-413

Citas:

---------- APA ----------

---------- CHICAGO ----------

---------- MLA ----------

---------- VANCOUVER ----------