Distribution based truncation for variable selection in subspace methods for multivariate regression

Chemometrics and Intelligent Laboratory Systems

Volumn 122, Issue , 2013, Pages 103-111

  Liland, Kristian Hovde ^a   Høy, Martin ^b   Martens, Harald ^a,b   Sæbø, Solve ^a  

^a NORWEGIAN UNIVERSITY OF LIFE SCIENCES   (Norway)

^b NOFIMA   (Norway)

Author keywords

Multivariate; Partial least squares; Relevant components; Sparse; Truncation; Variable selection

Indexed keywords

ALGORITHM; ANALYSIS OF VARIANCE; ARTICLE; CLINICAL EFFECTIVENESS; DATA ANALYSIS; HUMAN; INTERMETHOD COMPARISON; MATHEMATICAL COMPUTING; MULTIVARIATE ANALYSIS; PARTIAL LEAST SQUARES REGRESSION; PREDICTION; PRIORITY JOURNAL; PROCESS DEVELOPMENT; PROCESS MODEL; STATISTICAL DISTRIBUTION; STRATEGIC PLANNING; SYSTEMATIC ERROR; VALIDATION PROCESS;

EID: 84874320729     PISSN: 01697439     EISSN: 18733239     Source Type: Journal    
DOI: 10.1016/j.chemolab.2013.01.008     Document Type: Article

References (33)

1. Wold S., Martens H., Wold H. The multivariate calibration problem in chemistry solved by the PLS methods. Lecture Notes in Mathematics 1983, 973:286-293.
2. Indahl U. A twist to partial least squares regression. Journal of Chemometrics 2005, 19:32-44.
3. Liland K.H., Mevik B.-H., Rukke E.-O., Almøy T., Isaksson T. Quantitative whole spectrum analysis with MALDI-TOF MS, part II: determining the concentration of milk in mixtures. Chemometrics and Intelligent Laboratory Systems 2009, 99:39-48.
4. Tibshirani R. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society, Series B 1996, 58:267-288.
5. Zou H., Hastie T. Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society, Series B 2005, 67:301-320.
6. Sæbø S., Almøy T., Aarøe J., Aastveit A.H. ST-PLS: a multi-directional nearest shrunken centroid type classifier via PLS. Journal of Chemometrics 2008, 22:54-62.
7. Lê Cao K., Rossouw D., Robert-Granié C., Besse P. A sparse PLS for variable selection when integrating omics data. Statistical Applications in Genetics and Molecular Biology 2008, 7.
8. Wold S., Johansson E., Cocchi M. 3D QSAR in Drug Design: Theory, Methods and Applications 1993, ESCOM Science Publishers B.V., Leiden, The Netherlands.
9. Rajalahti T., et al. Discriminating variable test and selectivity ratio plot: quantitative tools for interpretation and variable (biomarker) selection in complex spectral or chromatographic profiles. Analytical Chemistry 2009, 81:2581-2590.
10. Martens H., Næs T. Multivariate Calibration 1989, John Wiley and Sons, Chichester, UK.
11. Lenth R.V. Quick and easy analysis of unreplicated factorials. Technometrics 1989, 31:469-473.
12. Wold H. Estimation of Principal Component and Related Models by Iterative Least Squares, Vol. Multivariate Analysis 1966, Academic Press, New York, USA.
13. Jørstad T., Midelfart H., Bones A. A mixture model approach to sample size estimation in two-sample comparative microarray experiments. BMC Bioinformatics 2008, 9.
14. Mehmood T., Liland K.H., Snipen L., Sæbø S. A review of variable selection methods in partial least squares regression. Chemometrics and Intelligent Laboratory Systems 2012, 118:62-69.
15. Roger J., Palagos B., Bertrand D., Fernandez-Ahumada E. CovSel: variable selection for highly multivariate and multi-response calibration: application to IR spectroscopy. Chemometrics and Intelligent Laboratory Systems 2011, 106:216-223.
16. Friedman J., Hastie T. Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software 2010, 33.
17. Chong I., Jun C. Performance of some variable selection methods when multicollinearity is present. Chemometrics and Intelligent Laboratory Systems 2005, 78:103-112.
18. Chun H. Keleş Sparse partial least squares regression for simultaneous dimension reduction and variable selection. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 2010, 72:3-25.
19. Lee D., Lee W., Lee Y., Pawitan Y. Sparse partial least-squares regression and its applications to high-throughput data analysis. Chemometrics and Intelligent Laboratory Systems 2011, 109:1-8.
20. Helland I.S., Almøy T. Comparison of prediction methods when only a few components are relevant. Journal of the American Statistical Association 1994, 89:583-591.
21. Næs T., Helland I.S. Relevant components in regression. Scandinavian Journal of Statistics 1993, 20:239-250.
22. Qiao Z., Zhou L., Huang J.Z. Sparse linear discriminant analysis with applications to high dimensional low sample size data. International Journal of Applied Mathematics 2009, 39:48-60.
23. Filzmosera P., Gschwandtnera M., Todorov V. Review of sparse methods in regression and classification with application to chemometrics. Journal 2012, 26:42-51.
24. Alon U., et al. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proceedings of the National Academy of Science 1996, 96:6745-6750.
25. Singh D., et al. Gene expression correlates of clinical prostate cancer behavior. Cancer Cell 2002, 1:203-209.
26. Afseth N.K., Segtnan V.H., Wold J.P. Raman spectra of biological samples: a study of preprocessing methods. Applied Spectroscopy 2006, 60:1358-1367.
27. Eilers P.H. Parametric time warping. Analytical Chemistry 2004, 76:404-411.
28. Liland K.H., Almøy T., Mevik B.-H. Optimal choice of baseline correction for multivariate calibration of spectra. Applied Spectroscopy 2010, 64:1007-1016.
29. Liland K.H., Rukke E.-O., Olsen E.F., Isaksson T. Customized baseline correction. Chemometrics and Intelligent Laboratory Systems 2011, 109:51-56.
30. Indahl U.G., Liland K.H., Næs T. Canonical partial least squares - a unified PLS approach to classification and regression problems. Journal of Chemometrics 2009, 23:495-504.
31. Stone M. Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society, Series B Methodological 1974, 36:111-147.
32. Pearson K. On lines and planes of closest fit to systems of points in space. Philosophical Magazine 1901, 2:559-572.
33. Comon P. Independent component analysis, a new concept?. Signal Processing 1994, 36:287-314.