Réduction de dimension dans les modèles linéaires généralisés : application à la classification supervisée de données issues des biopuces

Gersende Fort; Sophie Lambert-Lacroix; Julie Peyre

Journal de la société française de statistique (2005)

  • Volume: 146, Issue: 1-2, page 117-152
  • ISSN: 1962-5197

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Fort, Gersende, Lambert-Lacroix, Sophie, and Peyre, Julie. "Réduction de dimension dans les modèles linéaires généralisés : application à la classification supervisée de données issues des biopuces." Journal de la société française de statistique 146.1-2 (2005): 117-152. <http://eudml.org/doc/199652>.

@article{Fort2005,
author = {Fort, Gersende, Lambert-Lacroix, Sophie, Peyre, Julie},
journal = {Journal de la société française de statistique},
language = {fre},
number = {1-2},
pages = {117-152},
publisher = {Société française de statistique},
title = {Réduction de dimension dans les modèles linéaires généralisés : application à la classification supervisée de données issues des biopuces},
url = {http://eudml.org/doc/199652},
volume = {146},
year = {2005},
}

TY - JOUR
AU - Fort, Gersende
AU - Lambert-Lacroix, Sophie
AU - Peyre, Julie
TI - Réduction de dimension dans les modèles linéaires généralisés : application à la classification supervisée de données issues des biopuces
JO - Journal de la société française de statistique
PY - 2005
PB - Société française de statistique
VL - 146
IS - 1-2
SP - 117
EP - 152
LA - fre
UR - http://eudml.org/doc/199652
ER -

References

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  1. ALBERT A., ANDERSON J. (1984). On the Existence of Maximum Likelihood Estimates in Logistic Regression Models. Biometrika, 71(1) :1-10. Zbl0543.62020MR738319
  2. ALON U., BARKAI N., NOTTERMAN D., GISH K., YBARRA S., MACK D., LEVINE A. (1999). Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc. Natl. Acad. Sci. USA, 96(12) :6745-6750. 
  3. ANTONIADIS A., LAMBERT-LACROIX S., LEBLANC F. (2003). Effective Dimension Reduction Methods for Tumor - Classification using gene Expression Data. Bioinformatics, 19(5) :563-570. 
  4. BARKER M., RAYENS W. (2003). Partial least squares for discrimination. J. Chemometrics, 17 :166-173. 
  5. BASTIEN P. (2004). PLS-Cox model : application to gene expression. Dans Proceedings in Computational Statistics, pages 655-662. Physica-Verlag, Springer. MR2173059
  6. BASTIEN P., ESPOSITO VINZI V., TENENHAUS M. (2004). PLS generalised linear regression. Comput. Stat. Data Anal., 48(1) :17-46. Zbl05374090MR2133599
  7. BULL S., MAK C., GREENWOOD C. (2001). A modified score function estimator for multinomial logistic regression is small samples. Comput. Stat. Data Anal., 39 :57-74. Zbl1119.62344
  8. DEJONG S. (1995). PLS shrinks. J. Chemometrics, 9 :323-326. 
  9. DENHAM M. (2000). Choosing the number of factors in partial least squares regression : estimating and minimizing the mean squared error of prediction. J. Chemometrics, 14 :351-361. 
  10. DEVROYE L., GYORFI L., LUGOSI G. (1996). A Probabilistic Theory of Pattern Recognition. Springer-Verlag, New-York. Zbl0853.68150MR1383093
  11. DING B., GENTLEMAN R. (2005). Classification Using Generalized Partial Least Squares. J. Comp. Graph. Stat. À paraître. MR2160814
  12. DUDOIT S., FRIDLYAND J., SPEED T. (2002). Comparison of discrimination methods for the classification of tumors using gene expression data. J. Am. Stat. Assoc., 97(457) :77-87. Zbl1073.62576MR1963389
  13. EFRON B., HASTIE T., JOHNSTONE I., TIBSHIRANI R. (2004). Least angle regression. Ann. Stat., 32(2) :407-499. Zbl1091.62054MR2060166
  14. FAHRMEIR L., TUTZ G. (2001). Multivariate statistical modelling based on generalized linear models. 2nd ed. Springer Series in Statistics. New York. Zbl0980.62052MR1832899
  15. FAN J., GIJBELS I. (1996). Local polynomial modelling and its applications. Monographs on Statistics and Applied Probability. Chapman and Hall, London. Zbl0873.62037MR1383587
  16. FORT G. (2005). Partial Least Squares for Classification and Feature selection in Microarray gene expression data. Soumis. 
  17. FORT G., LAMBERT-LACROIX S. (2005). Classification using Partial Least Squares with Penalized Logistic Regression. Bioinformatics, 21(7) :1104-1111. 
  18. FRANK I., FRIEDMAN J. (1993). A statistical view of some chemometrics regression tools, with discussion. Technometrics, 35(2) :109-148. Zbl0775.62288
  19. GARTHWAITE P. (1994). An interpretation of partial least squares. J. Am. Stat. Assoc., 89(425) :122-127. Zbl0793.62034MR1266290
  20. GOLUB T., SLONIM D., TAMAYO P., HUARD C., GAASENBEEK M., MESIROV J., COLLER H., LOH M., DOWNING J., CALIGIURI M., BLOOMFIELD C., LANDER E. (1999). Molecular Classification of Cancer : Class Discovery and Class Prediction by Gene Expression Monitoring. Science, 286(5439) :531-537. 
  21. GOUTIS C. (1996). Partial Least Squares algorithm yields shrinkage estimators. Ann. Stat., 24(2) :816-824. Zbl0859.62067MR1394990
  22. GREEN P. (1984). Iteratively Reweighted Least Squares for Maximum Likelihood Estimation, and some Robust and Resistant Alternatives. J. R. Stat. Soc., Ser. B, 46(2) :149-192. Zbl0555.62028MR781879
  23. GUYON I., WESTON J., BARNHILL S., VAPNIK V. (2002). Gene Selection for Cancer Classification using Support Vector Machines. Mach. Learn., 46(1-3) :389-422. Erratum : http://clopinet.com/isabelle/Papers/index.html. Zbl0998.68111
  24. HASTIE T., TIBSHIRANI R. (1990). Generalized Additive Models. Monographs on Statistics and Applied Probability. New York : Chapman and Hall. Zbl0747.62061MR1082147
  25. HASTIE T., TIBSHIRANI R., EISEN M., ALIZADEH A., LEVY R., STAUDT L., CHAN W., BOTSTEIN D., BROWN P. (2000). 'gene shaving' as a method for identifying distinct sets of genes with similar expression patterns. Genome Biol., 1. 
  26. HELLAND I. (1988). On the structure of Partial Least Squares regression. Commun. Stat. Simulation Comput., 17(2) :581-607. Zbl0695.62167MR955342
  27. HELLAND I. (1990). Partial Least Squares Regression and Statistical Models. Scand. J. Statist., 17(2) :97-114. Zbl0713.62062MR1085924
  28. KUO W., KIM E., TRIMARCHI B., JENSSEN J., VINTERBO T., OHNO-MACHADO L. (2004). Bayes factor. J. Biomed. Inform., 37 :293-303. 
  29. LAMBERT-LACROIX S., PEYRE J. (2005). Local quasi-likelihood regression in generalized single-index models. Travaux en cours. Zbl1157.62384
  30. LESAFFRE E., ALBERT A. (1989). Partial separation in logistic discrimination. J. R. Stat. Soc., Ser. B, 51(1) :109-116. Zbl0669.62044MR984997
  31. LINGJAERDE O., CHRISTOPHERSEN N. (2000). Shrinkage structure of Partial Least Squares. Scand. J. Stat., 27 :459-473. Zbl0972.62044MR1795775
  32. MARX B. D. (1996). Iteratively Reweighted Partial Least Squares estimation for Generalized Linear Regression. Technometrics, 38(4) :374-381. Zbl0902.62081
  33. NGUYEN D., ROCKE D. ( 2002a). Multi-class cancer classification via Partial Least Squares with gene expression profiles. Bioinformatics, 18(9) :1116-1226. 
  34. NGUYEN D., ROCKE D. ( 2002b). Tumor classification by Partial Least Squares using microarray gene expression data. Bioinformatics, 18(1) :39-50. 
  35. NGUYEN D., ROCKE D. (2004). On partial least squares dimension reduction for microarray-based classification : a simulation study. Comput. Stat. Data Anal., 46 :407-425. Zbl05374013MR2067030
  36. PHATAK A., REILLY P., PENLIDIS A. (2002). The asymptotic variance of the univariate PLS estimator. Linear Algebra Appl., 354(1-3) :245-253. Zbl1022.62024MR1927660
  37. SANTNER T., DUFFY D. (1986). A note on A. Albert and J.A. Anderson's Conditions for the Existence of Maximum Likelihood Estimates in Logistic Regression Models. Biometrika, 73(3) :755-758. Zbl0655.62022MR897873
  38. SAPORTA G. (1990). Probabilités, analyse des données et statistique. Paris : Éditions Technip. Zbl0703.62003
  39. SCHWARZ G. (1978). Estimating the dimension of a model. Ann. Stat., 6(2) :461-464. Zbl0379.62005MR468014
  40. SEIFERT B., GASSER T. (1996). Finite sample variance of local polynomials : Analysis and solutions. J. Am. Stat. Assoc., 91(433) :267-275. Zbl0871.62042MR1394081
  41. STOICA P., SODERSTROM T. (1998). Partial Least Squares : A First-order Analysis. Scand. J. Stat., 25(1) :17-24. Zbl0903.62057MR1614231
  42. STONE M., BROOKS R. (1990). Continuum regression : Cross-validated sequentially constructed prediction embracing ordinary least squares, partial least squares and principal components regression. J. R. Stat. Soc., Ser. B, 52(2) :237-269. Zbl0708.62054MR1064418
  43. TIBSHIRANI R. (1996). Regression Shrinkage and Selection via the Lasso. J. R. Stat. Soc., Ser. B, 58(1) :267-288. Zbl0850.62538MR1379242
  44. XIA Y., TONG H., LI W., ZHU L. (2002). An adaptive estimation of dimension reduction space. J. R. Stat. Soc., Ser. B, 64(3) :363-410. Zbl1091.62028MR1924297
  45. ZHU J., HASTIE T. (2004). Classification of Gene Microarrays by Penalized Logistic Regression. Biostatistics, 5 :427-443. Zbl1154.62406
  46. ZOU H., HASTIE T. (2005). Regularization and Variable Selection via the Elastic Net. J. R. Stat. Soc., Ser. B, 67(2) :301-320. Zbl1069.62054MR2137327

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