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Referencias:

• Bali, L., Boente, G., Tyler, D., Wang, J.L., Robust Functional Principal Components: A Projection-Pursuit Approach (2011) The Annals of Statistics, 39, pp. 2852-2882
• Becker, C., Gather, U., The Largest Nonidentifiable Outliers: A Comparison of Multivariate Simultaneous Outliers Identification Rules (2001) Computational Statistics and Data Analysis, 36, pp. 119-127
• Boente, G., Asymptotic Theory for Robust Principal Components (1987) Journal of Multivariate Analysis, 21, pp. 67-78
• Boente, G., Salibian-Barrera, M., Tyler, D., A Characterization of Elliptical Distributions and Some Optimality Properties of Principal Components for Functional Data (2014) Journal of Multivariate Analysis, 131, pp. 254-264
• Campbell, N.A., Robust Procedures in Multivariate Analysis I: Robust Covariance Estimation (1980) Applied Statistics, 29, pp. 231-237
• Candès, E.J., Li, X., Ma, Y., Wright, J., Robust Principal Component Analysis? (2011) Journal of the ACM, 58, pp. 1-37
• Chandrasekaran, V., Sanghavi, S., Parrilo, P., Willsky, A.S., Rank-Sparsity Incoherence for Matrix Decomposition (2011) SIAM Journal of Optimization, 21, pp. 572-596
• Croux, C., Filzmoser, P., Pison, G., Rousseeuw, P.J., Fitting Multiplicative Models by Robust Alternating Regressions (2003) Statistics and Computing, 13, pp. 23-36
• 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
• Croux, C., High-Breakdown Estimators for Principal Components: The Projection-Pursuit Approach Revisited (2005) Journal of Multivariate Analysis, 95, pp. 206-226
• Cui, H., He, X., Ng, K.W., Asymptotic Distribution of Principal Components Based on Robust Dispersions (2003) Biometrika, 90, pp. 953-966
• Torre, F., Black, M.J., Robust Principal Components Analysis for Computer Vision (2001) Proceedings of the 8th International Conference on Computer Vision, 1, pp. 362-369
• 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
• Febrero, M., Galeano, P., Gonzalez-Manteiga, W., A Functional Analysis of NOx Levels: Location and Scale Estimation and Outlier Detection (2007) Computational Statistics, 22, pp. 411-427
• Febrero, M., Outlier Detection in Functional Data by Depth Measures, With Application to Identify Abnormal Nox Levels (2008) Environmetrics, 19, pp. 331-345
• Gervini, D., Robust Functional Estimation Using the Spatial Median and Spherical Principal Components (2008) Biometrika, 95, pp. 587-600
• Hardin, J., Rocke, D., The Distribution of Robust Distances (2005) Journal of Computational and Graphical Statistics, 14, pp. 1-19
• Huber, P.J., Ronchetti, E.M., (2009) Robust Statistics, , 2nd ed., New York: Wiley
• Hubert, M., Rousseeuw, P.J., Branden, K., ROBPCA: A New Approach to Robust Principal Component Analysis (2005) Technometrics, 47, pp. 64-79
• Hubert, M., Rousseeuw, P.J., Verboven, S., A Fast Method for Robust Principal Components With Applications to Chemometrics (2002) Chemometrics and Intelligent Laboratory Systems, 60, pp. 101-111
• Hubert, M., Vandervieren, E., An Adjusted Boxplot for Skewed Distributions (2008) Computational Statistics and Data Analysis, 52, pp. 5186-5201
• Hyndman, R.J., Ullah, S., Robust Forecasting of mortality and Fertility Rates: A Functional Data Approach (2007) Computational Statistics and Data Analysis, 51, pp. 4942-4956
• Hyndman, R.J., Shang, H.L., Rainbow Plots, Bagplots, and Boxplots for Functional Data (2010) Journal of Computational and Graphical Statistics, 19, pp. 29-45
• Lerman, G., McCoy, M., Tropp, J.A., Zhang, T., Robust Computation of Linear Models, or How to Find a Needle in a Haystack (2014) Foundations of Computational Mathematics, , 15, 363–410
• 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., Ghosh, S., Young, 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., Principal Components and Orthogonal Regression Based on Robust Scales (2005) Technometrics, 47, pp. 264-273
• Maronna, R., Martin, R.D., Yohai, V., (2006) Robust Statistics: Theory and Methods, , Chichester, UK: Wiley
• Maronna, R., Yohai, V., Robust Lower–Rank Approximation of Data Matrices With Element–Wise Contamination (2008) Technometrics, 50, pp. 295-304
• McCoy, M., Tropp, J.A., Two Proposals for Robust PCA Using Semidefinite Programming (2011) Electronic Journal of Statistics, 5, pp. 1123-1160
• Naga, R., Antille, G., Stability of Robust and Non–Robust Principal Component Analysis (1990) Computational Statistics and Data Analysis, 10, pp. 169-174
• Pison, G., van Aelst, S., Diagnostic Plots for Robust Multivariate Methods (2004) Journal of Computational and Graphical Statistics, 13, pp. 1-20
• Rousseeuw, P.J., van Driessen, K., A Fast Algorithm for the Minimum Covariance Determinant Estimator (1999) Technometrics, 41, pp. 212-223
• Rousseeuw, P.J., Van Zomeren, B.C., Unmasking Multivariate Outliers and Leverage Points (1990) Journal of the American Statistical Association, 85, pp. 633-651
• Salibian-Barrera, M., Yohai, V.J., A Fast Algorithm for S-Regression Estimates (2006) Journal of Computational and Graphical Statistics, 15, pp. 414-427
• Sawant, P., Billor, N., Shin, H., Functional Outlier Detection With Robust Functional Principal Component Analysis (2012) Computational Statistics, 27, pp. 83-102
• Verboon, P., Heiser, W.J., Resistant Lower-Rank Approximation of Matrices by Iterative Majorization (1994) Computational Statistics and Data Analysis, 18, pp. 457-467
• Xu, H., Caramanis, C., Sanghavi, S., Robust PCA via Outlier Pursuit (2012) IEEE Transactions on Information Theory, 58, pp. 3047-3064
• Zhang, T., Lerman, G., A Novel M-Estimator for Robust PCA (2014) Journal of Machine Learning Research, 15, pp. 749-808

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