logo_2Cy.gif
Home About us Media Research Consultancy Training Site map Contact

Home » Research » Component selection » References

  1. K. Baumann, H. Albert and M. von Korff, A systematic evaluation of the benefits and hazards of variable selection in latent variable regression. Part I. Search algorithm, theory and simulations, Journal of Chemometrics, 16 (2002) 339-350.
  2. K. Baumann, M. von Korff and H. Albert, A systematic evaluation of the benefits and hazards of variable selection in latent variable regression. Part II. Practical applications, Journal of Chemometrics, 16 (2002) 351-360.
  3. K. Baumann, Cross-validation as the objective function for variable selection techniques, Trends in Analytical Chemistry, 22 (2003) 395-406.
  4. K. Baumann and N. Stiefl, Validation tools for variable subset regression, Journal of Computer-Aided Molecular Design, 18 (2004) 549-562.
  5. M.C. Denham, Choosing the number of factors in partial least squares regression: estimating and minimizing the mean squared error of prediction, Journal of Chemometrics, 14 (2000) 351-361.
  6. K. Faber and B.R. Kowalski, Propagation of measurement errors for the validation of predictions obtained by principal component regression and partial least squares, Journal of Chemometrics, 11 (1997) 181-238.
  7. N.M. Faber, A closer look at the bias-variance trade-off in multivariate calibration, Journal of Chemometrics, 13 (1999) 185-192.
  8. N.M. Faber, Critical evaluation of a significance test for partial least squares regression, Analytica Chimica Acta, 432 (2001) 235-240.
  9. S.Z. Fairchild and J.H. Kalivas, PCR eigenvector selection based on correlation relative standard deviations, Journal of Chemometrics, 15 (2001) 615-625.
  10. M. Forina, S. Lanteri, M.C. Cerrato Oliveros and C. Pizarro Millan, Selection of useful predictors in multivariate calibration, Analytical and Bioanalytical Chemistry, 380 (2004) 397-418.
  11. S. Geisser, A predictive approach to the random effect model, Biometrika, 61 (1974) 101-107.
  12. S. Gourvénec, J.A. Fernández Pierna, D.L. Massart and D.N. Rutledge, An evaluation of the PoLiSh smoothed regression and the Monte Carlo cross-validation for the determination of the complexity of a PLS model, Chemometrics and Intelligent Laboratory Systems, 68 (2003) 41-51.
  13. R.L. Green and J.H. Kalivas, Graphical diagnostics for regression model determinations with consideration of the bias/variance trade-off, Chemometrics and Intelligent Laboratory Systems, 60 (2002) 173-188.
  14. D.M. Haaland and E.V. Thomas, Partial least-squares methods for spectral analyses. 1. Relation to other quantitative calibration methods and the extraction of qualitative information, Analytical Chemistry, 60 (1988) 1193-1202.
  15. T.R. Holcomb, H. Hjalmarsson, M. Morari and M.L. Tyler, Significance regression: a statistical approach to partial least squares, Journal of Chemometrics, 11 (1997) 283-309.
  16. A. Höskuldsson, Dimension of linear models, Chemometrics and Intelligent Laboratory Systems, 32 (1996) 37-55.
  17. J.T. Hwang and D. Nettleton, Principal component regression with data-chosen components and related methods, Technometrics, 45 (2003) 70-79.
  18. A. Lazraq and R. Cléroux, The PLS multivariate regression model: testing the significance of successive PLS components, Journal of Chemometrics, 15 (2001) 523-536.
  19. S. Ledauphin, M. Hanafi and E.M. Qannari, Simplification and signification of principal components, Chemometrics and Intelligent Laboratory Systems, 74 (2004) 277-281.
  20. B. Li, J. Morris and E.B. Martin, Model selection for partial least squares regression, Chemometrics and Intelligent Laboratory Systems, 64 (2002) 79-89.
  21. D. Liu, S.L. Shah and D. Grant Fisher, Choice of latent explanatory variables: a multiobjective optimization approach, Journal of Chemometrics, 14 (2000) 79-92.
  22. A. Lorber and B.R. Kowalski, Alternatives to cross-validatory estimation of the number of factors in multivariate calibration, Applied Spectroscopy, 44 (1990) 1464-1470.
  23. H. Martens and T. Næs, Multivariate calibration by data compression. In Near-infrared Technology in the Agricultural and Food Industries, P. Williams and K. Norris (eds). American Cereal Association: St. Paul, MN, 1987; 57-87.
  24. H.A. Martens and P. Dardenne, Validation and verification of regression in small data sets, Chemometrics and Intelligent Laboratory Systems, 44 (1998) 99-121.
  25. N.J. Messick , J.H. Kalivas and P.M. Lang, Selecting factors for partial least squares, Microchemical Journal, 55 (1997) 200-207.
  26. D.W. Osten, Selection of optimal regression models via cross-validation, Journal of Chemometrics, 2 (1988) 39-48.
  27. R.R. Picard and R.D. Cook, Cross-validation of regression models, Journal of the American Statistical Association, 79 (1984) 575-583.
  28. J.B. Reeves III and S.R. Delwiche, SAS® partial least squares regression for analysis of spectroscopic data, Journal of Near Infrared Spectroscopy, 11 (2003) 415-431.
  29. D.N. Rutledge, A. Barros and I. Delgadillo, PoLiSh—smoothed partial least-squares regression, Analytica Chimica Acta, 446 (2001) 281-296.
  30. H. Seipel and J.H. Kalivas, Effective rank for multivariate calibration methods, Journal of Chemometrics, 18 (2004) 306-311.
  31. J. Shao, Linear model selection by cross-validation, Journal of the American Statistical Association, 88 (1993) 486-494.
  32. R.P. Sheridan, R.B. Nachbar and B.L. Bush, Extending the trend vector: The trend matrix and sample-based partial least squares, Journal of Computer-Aided Molecular Design, 8 (1994) 323-340.
  33. M. Stone, Cross-validatory choice and assessment of statistical predictions, Journal of the Royal Statistical Society B, 36 (1974) 111-133.
  34. J.M. Sutter, J.H. Kalivas and P.M. Lang, Which principal components to utilize for principal component regression, Journal of Chemometrics, 6 (1992) 217-225.
  35. J. Thioulouse and J.R. Lobry, Co-inertia analysis of amino-acid physico-chemical properties and protein composition with the ADE package, Computer Applications in the Biosciences, 11 (1995) 321-329.
  36. E.V. Thomas, Non-parametric statistical methods for multivariate calibration model selection and comparison, Journal of Chemometrics, 17 (2003) 653-659.
  37. H. van der Voet, Comparing the predictive accuracy of models using a simple randomization test, Chemometrics and Intelligent Laboratory Systems, 25 (1994) 313-323.
  38. H. van der Voet, Corrigendum to "Comparing the predictive accuracy of models using a simple randomization test", Chemometrics and Intelligent Laboratory Systems, 28 (1995) 315.
  39. I.N. Wakeling and J.J. Morris, A test of significance for partial least squares regression, Journal of Chemometrics, 7 (1993) 291-304.
  40. F. Westad and H. Martens, Variable selection in near infrared spectroscopy based on significance testing in partial least squares regression, Journal of Near Infrared Spectroscopy, 8 (2000) 117-124.
  41. S. Wold, Cross-validatory estimation of the number of components in factor and principal components models, Technometris, 20 (1978) 397-405.
  42. Q.-S. Xu and Y.-Z. Liang, Monte Carlo cross validation, Chemometrics and Intelligent Laboratory Systems, 56 (2001) 1-11.
  43. Q.-S. Xu, Y.-Z. Liang and Y.-P. Du, Monte Carlo cross-validation for selecting a model and estimating the prediction error in multivariate calibration, Journal of Chemometrics, 18 (2004) 112-120.
  44. J. Ye, On measuring and correcting the effects of data mining and model selection, Journal of the American Statistical Association, 93 (1998) 120-131.
  45. P. Zhang, Model selection via multifold cross validation, Annals of Statistics, 21 (1993) 299-313.