A fast method for robust principal components with applications to chemometrics

[1]  P. Rousseeuw,et al.  Robust factor analysis , 2003 .

[2]  C. Croux,et al.  Principal Component Analysis Based on Robust Estimators of the Covariance or Correlation Matrix: Influence Functions and Efficiencies , 2000 .

[3]  Katrien van Driessen,et al.  A Fast Algorithm for the Minimum Covariance Determinant Estimator , 1999, Technometrics.

[4]  D. G. Simpson,et al.  Robust principal component analysis for functional data , 2007 .

[5]  Paul H. C. Eilers,et al.  Generalized linear regression on sampled signals and curves: a P -spline approach , 1999 .

[6]  S. D. Jong,et al.  The kernel PCA algorithms for wide data. Part I: Theory and algorithms , 1997 .

[7]  P. Rousseeuw,et al.  Alternatives to the Median Absolute Deviation , 1993 .

[8]  E. Ziegel.  COMPSTAT: Proceedings in Computational Statistics , 1988 .

[9]  Peter J. Rousseeuw,et al.  Robust Regression and Outlier Detection , 2005, Wiley Series in Probability and Statistics.

[10]  P. L. Davies,et al.  Asymptotic behaviour of S-estimates of multivariate location parameters and dispersion matrices , 1987 .

[11]  Guoying Li,et al.  Projection-Pursuit Approach to Robust Dispersion Matrices and Principal Components: Primary Theory and Monte Carlo , 1985 .

[12]  P. Rousseeuw.  Least Median of Squares Regression , 1984 .

[13]  T. Fearn,et al.  Application of near infrared reflectance spectroscopy to the compositional analysis of biscuits and biscuit doughs , 1984 .

[14]  S. J. Devlin,et al.  Robust estimation and outlier detection with correlation coefficients , 1975 .