Chapter 36 - Multivariate calibration

[1]  Yu-Long Xie,et al.  Evaluation of principal component selection methods to form a global prediction model by principal component regression , 1997 .

[2]  Philip K. Hopke,et al.  The chemical mass balance as a multivariate calibration problem , 1997 .

[3]  Bruce R. Kowalski,et al.  Propagation of measurement errors for the validation of predictions obtained by principal component regression and partial least squares , 1997 .

[4]  R. Wehrens,et al.  Bootstrapping principal component regression models , 1997 .

[5]  Evelyne Vigneau,et al.  Application of latent root regression for calibration in near-infrared spectroscopy. Comparison with principal component regression and partial least squares , 1996 .

[6]  D. Massart,et al.  Elimination of uninformative variables for multivariate calibration. , 1996, Analytical chemistry.

[7]  Anita Singh Outliers and robust procedures in some chemometric applications , 1996 .

[8]  D. Massart,et al.  Standardisation of near-infrared spectrometric instruments: A review , 1996 .

[9]  Riccardo Leardi,et al.  Genetic Algorithms as a Tool for Wavelength Selection in Multivariate Calibration , 1995 .

[10]  Bruce R. Kowalski,et al.  Additive Background Correction in Multivariate Instrument Standardization , 1995 .

[11]  Beata Walczak,et al.  Outlier detection in bilinear calibration , 1995 .

[12]  R. Brooks,et al.  Joint Continuum Regression for Multiple Predictands , 1994 .

[13]  Onno E. de Noord,et al.  Multivariate calibration standardization , 1994 .

[14]  Hilko van der Voet,et al.  Comparing the predictive accuracy of models using a simple randomization test , 1994 .

[15]  Paolo Conti,et al.  Validation procedures in near-infrared spectrometry , 1994 .

[16]  Onno E. de Noord,et al.  The influence of data preprocessing on the robustness and parsimony of multivariate calibration models , 1994 .

[17]  T. Isaksson,et al.  New approach for distance measurement in locally weighted regression , 1994 .

[18]  J. Friedman,et al.  A Statistical View of Some Chemometrics Regression Tools , 1993 .

[19]  Tormod Naes,et al.  Detecting and adjusting for non‐linearities in calibration of near‐infrared data using principal components , 1993 .

[20]  Barry J. Wythoff,et al.  Backpropagation neural networks , 1993 .

[21]  H. J. H. Macfie,et al.  A robust PLS procedure , 1992 .

[22]  John H. Kalivas,et al.  Which principal components to utilize for principal component regression , 1992 .

[23]  Agnar Höskuldsson,et al.  The H-principle in modelling with applications to chemometrics , 1992 .

[24]  S. Wold Nonlinear partial least squares modelling II. Spline inner relation , 1992 .

[25]  Henk A. L. Kiers,et al.  Principal covariates regression: Part I. Theory , 1992 .

[26]  H. H. Thodberg,et al.  Optimal minimal neural interpretation of spectra , 1992 .

[27]  Clifford H. Spiegelman,et al.  Chemometrics and spectral frequency selection , 1991, Philosophical Transactions of the Royal Society of London. Series A: Physical and Engineering Sciences.

[28]  B. Kowalski,et al.  Multivariate instrument standardization , 1991 .

[29]  Bruce R. Kowalski,et al.  Recent developments in multivariate calibration , 1991 .

[30]  Tomas Isaksson,et al.  Splitting of calibration data by cluster analysis , 1991 .

[31]  S. Wold,et al.  Nonlinear PLS modeling , 1989 .

[32]  Rolf Sundberg,et al.  Interplay between chemistry and statistics, with special reference to calibration and the generalized standard addition method☆ , 1988 .

[33]  I. E. Frank Intermediate least squares regression method , 1987 .

[34]  P. Geladi,et al.  Linearization and Scatter-Correction for Near-Infrared Reflectance Spectra of Meat , 1985 .

[35]  P. T. Davies,et al.  Procedures for Reduced‐Rank Regression , 1982 .

[36]  David M. Allen,et al.  The Relationship Between Variable Selection and Data Agumentation and a Method for Prediction , 1974 .