Ridge penalized logistical and ordinal partial least squares regression for predicting stroke deficit from infarct topography

References

[1] Barber, P.A., Darby, D.G., Desmond, P.M., Yang, Q., Gerraty, R.P., Jolley, D., Donnan, G.A., Tress, B.M. and Davis, S.M. (1998) Prediction of stroke outcome with echoplanar perfusion- and diffusion-weighted MRI. Neurology, 51(2), 418-426.

[2]
Wardlaw, J.M., Keir, S.L., Bastin, M.E., Armitage, P.A. and Rana, A.K. (2002) Is diffusion imaging appearance an independent predictor of outcome after ischemic stroke? Neurology, 59(9), 1381-1387.

[3]
Kertesz, A. (1979) Aphasia and associated disorder: Taxonomy, localization and recovery. Grune & Stratton, Inc., New York.

[4]
Dronkers N.F. (1996) A new brain region for coordinating speech articulation. Nature, 384, 159-161.

[5]
Bates, E. Wilson, S.M. Saygin, A.P. Dick, F. Sereno, M.I. Knight, R.T. and Dronkers, N.F. (2003) Voxel-based lesion-symptom mapping. Nature Neuroscience, 6(5), 448-450.

[6]
Frank, I. and Friedman, J. (1993) A statistic review of some chemometrics regression tools, with discussion, Technometrics, 35(2), 109-148.

[7]
Wold, H. (1975) Soft modelling by latent variables: Non- linear iterative partial least squares (NIPALS) approach. In: Gani, M.S.B., Ed., Perspectives in Probability and Statistics, Academic Press, London, 117-142.

[8]
Naes, T. and Martens, H. (1985) Comparison of prediction methods for multicollinearity data. Communication Statist Assoc, 60, 234-246.

[9]
Bookstein, F.L. (1994) Partial least squares: A dose- response model for measurement in the behavioral and brain sciences. Psycoloquy, 5(23), least squares (1).

[10]
McIntoch, A.R., Bookstein, F.L., Haxby, J.C. and Grady, C.L. (1996) Spatial pattern analysis of functional brain images using partial least squares. Neuroimage, 3(3), 143-157.

[11]
Leibovitch, F.S., et al. (1999) Brain SPECT imaging and left hemispatial neglect covaried using partial least squares: the sunnybrook stroke study. Human Brain Mapping, 7(4), 244-253.

[12]
Fort, G. and Lambert-Lacroix, S. (2005) Classification using partial least squares with penalized logistic regres- sion. Bioinformatics, 21(7), 1104-1111.

[13]
Shen, L. and Tan, E.C. (2005) PLS and SVD based pena- lized logistic regression for cancer classification using microarray data. Proceedings of the 3rd Asia-Pacific Bioinformatics Conference, Singapore, 17-21 January 2005, 219-228.

[14]
Huang, X.H., Pan, W., Han, X.Q., Chen, Y.J., Miller, L.W. and Hall, J. (2005) Borrowing information from relevant microarray studies for sample classification using weighted partial least squares. Computational Biology and Chemistry, 29(3), 204-211.

[15]
Marx, B.D. (1996) Iterative reweighted least squares estimation for generalized linear regression. Techno- metrics, 38(4), 374-381.

[16]
Phan, T.G., Chen, J., Donnan, G., Srikanth, V., Wood, A. and Reutens, D.C. (2009) Development of a new tool to correlate stroke outcome with infarct topography: A proof- of-concept study. NeuroImage, 49(1), 127-133.

[17]
Draper, N.R. and Smith, H. (1998) Applied Regression Analysis, 3rd Edition, Wiley, New York.

[18]
Kutner, M.H., Neter, J., Nachtsheim, C.J. and Li, W. (2004) Applied linear statistical models, 5th Edition. McGraw- Hill Irwin, Boston.

[19]
Hoerl, A.E. and Kennard, R.W. (1970) Ridge regression: Biased estimation for nonorthogonal problems. Techno- metrics, 12(1), 55-67.

[20]
Le Cessie, S. and van Houwelingen, J.C. (1992) Ridge estimators in logistic regression, Applied Statistics, 41(1), 191-201.

[21]
Kass, R. and Raftery, A. (1995) Bayes factor. Journal of the American Statistical Association, 90(430), 773-795.

[22]
Talairach, J. and Tournoux, P. (1988) Co-planar stereo- tactic atlas of the human brain. Thieme Medical Publi- shers, New York.

[23]
Woods, R.P., Grafton, S.T., Watson, J.D., Sicotte, N.L. and Mazziotta, J.C. (1998) Automated image registration: II. Intersubject validation of linear and nonlinear models. Journal of Computer Assisted Tomography, 22(1), 153- 165.

[24]
Wold, S., Martens, H. and Wold, H. (1983) The multi- variate calibration problem in chemistry solved by the PLS method. In: Ruhe, A. and Kagstrom, B. Eds., Proceedings of the Conference on Matrix Pencils, Pite Havsbad, 22-24 March 1983, 286-293.

[25]
Geladi, P. and Kowalski, B.P. (1986) Partial least-squares regression: A tutorial. Analytica Chimica Acta, 185(1), 1-17.

[26]
Abdi, H. (2003) Partial least squares (PLS) regression. In Bryman, A. Futing, T. and Lewis-Beck, M. Eds., Ency- clopedia of Social Sciences Research Methods, London.