Robust Multivariate Calibration in Environmental Studies

References

• Martens , H. and Naes , T. 1989 . Multivariate Calibration 116 – 166 . New York : John Wiley & Sons .
   Google Scholar
• Wold , H. 1981 . “ Soft Modeling: The Basic Design and Some Extensions ” . In Systems Under Indirect Observation Edited by: Joreskog , K.G. and Wold , H. Vol. 2 , 1 – 53 . Amsterdam, , North Holland
   Google Scholar
• Wold , S. , Martens , H. and Wold , H. 1983 . The Multivariate Calibration Problem in Chemistry Solved by the PLS Method 286 – 293 . Heidelberg : Springer Verlag .
   Google Scholar
• Einax , J.W. , Truckenbrodt , D. and Kampe , O. 1998 . River pollution data interpreted by means of chemometric methods . Microchemical Journal , 58 : 315 – 324 .
   Google Scholar
• Einax , J.W. , Kampe , O. and Truckenbrodt , D. 1998 . Assessing the deposition and remobilization behavior of metals between river water and river sediment using partial least squares regression . Fresenius Journal of Analytical Chemistry , 361 : 149 – 154 .
   Google Scholar
• Joliffe , I.T. 1982 . A note on the use of principal components in regression . Applied Statistics , 31 : 300 – 303 .
   Google Scholar
• Rousseeuw , P.J. and Leroy , A.M. 1987 . Robust Regression and Outlier Detection New York : John Wiley & Sons .
   Google Scholar
• Chatterjee , S. and Hadi , A.S. 1986 . Influential observations, high leverage points, and outliers in linear regression . Statistical Science , 1 : 379 – 416 .
   Google Scholar
• Rousseeuw , P.J. 1984 . Least median of squares regression . Journal of the American Statistical Association , 79 : 871 – 880 .
   Google Scholar
• Rousseeuw , P.J. 1985 . “ Multivariate Estimation With High Breakdown Point ” . In Mathematical Statistics and Applications Edited by: Grossmann , W. , Pflung , G. , Vincze , I. and Wertz , W. 283 – 297 . Dordrecht : Reidel .
   Google Scholar
• Rousseeuw , P.J. and VanDriesen , K. 1999 . Determinant estimator . Technometrics , 41 : 212 – 223 .
   Google Scholar
• Massart , D.L. , Kaufman , L. , Rousseeuw , P.J. and Leroy , A. 1986 . Least median of squares: a robust method for outlier and model error detection in regression and calibration . Analitica Chimica Acta , 187 : 171 – 179 .
   Google Scholar
• Rousseeuw , P.J. and Yohai , V.J. 1984 . Least median of squares regression . Journal of American Statistical Association , 79 : 871 – 880 .
   Google Scholar
• Siegel , A.F. 1982 . Robust regression using repeated median . Biometrika , 69 : 242 – 244 .
   Google Scholar
• Ruppert , D. and Carroll , R.J. 1980 . Trimmed least squares estimation in linear model . Journal of American Statistical Association , 75 : 828 – 838 .
   Google Scholar
• Walczak , B. and Massart , D.L. 1995 . Robust principal components regression as a detection tool for outliers . Chemom. Intell. Lab. Sys. , 27 : 41 – 54 .
   Google Scholar
• Walczak , B. 1995 . Outlier detection in multivariate calibration . Chemom. Intell. Lab. Sys. , 28 : 259 – 272 .
   Google Scholar
• Walczak , B. 1995 . Outlier detection in bilinear calibration . Chemom. Intell. Lab. Sys. , 29 : 63 – 73 .
   Google Scholar
• Goldberg , D.E. 1989 . Genetic Algorithms in Search Optimization and Machine Learning New York : Addison-Wesley .
   Google Scholar
• Lucasius , C.B. and Kateman , G. 1993 . Understanding and using genetic algorithms. Part I. Concepts, properties, and context . Chemom. Intell. Lab. Sys. , 19 : 1 – 33 .
   Google Scholar
• Wold , S. 1978 . Cross-validatory estimation of the number of components in factor and principal components models . Technometrics , 20 : 397 – 405 .
   Google Scholar
• Michalewicz , Z. 1992 . Genetic Algorithms + Data Structures = Evolution Programs 139 – 241 . New York : Springer-Verlag .
   Google Scholar
• Truckenbrodt , D. , Kampe , O. and Einax , J.W. 1996 . Analyti und Bewertung des Belastungszustandes der Saale Ilm und Unstrut . Vom Wasser , 87 : 29 – 38 .
   Google Scholar
• Truckenbrodt , D. , Kampe , O. and Einax , J.W. 1995 . “ Zur aktuellen Belastungssituation der Saale, Ilm und Unstrut ” . In Die Belastung der Elbe: Teil I. Elbenebenflusse 57 – 68 . Karlsruhe : Forschungszentrum Karlruhe, BMBF .
   Google Scholar