References

Abdi, H. and L.J. Williams, 2010. Principal component analysis. Wiley Interdiscip. Rev. Comput. Stat., 2: 433-459.
CrossRef  |  Direct Link  |  

Allison, P., 2012. When can you safely ignore multicollinearity. Stat. Horiz., Vol. 5,

Amendolia, S.R., G. Cossu, M.L. Ganadu, B. Golosio and G.L. Masala et al., 2003. A comparative study of K-nearest neighbour, support vector machine and multi-layer perceptron for thalassemia screening. Chemom. Intell. Lab. Syst., 69: 13-20.
Direct Link  |  

Anderson, J.A., 1972. Separate sample logistic discrimination. Biometrics, 59: 19-35.
Direct Link  |  

Ball, N.M. and R.J. Brunner, 2010. Data mining and machine learning in astronomy. Intl. J. Mod. Phys. D., 19: 1049-1106.
Direct Link  |  

Ballabio, D. and R. Todeschini, 2009. Multivariate classification for qualitative analysis. Infrared Spectrosc. Food Qual. Anal. Control, 1: 83-104.

Bauer, D.J., 2005. A semiparametric approach to modeling nonlinear relations among latent variables. Struct. Equ. Model., 12: 513-535.
Direct Link  |  

Cawley, G.C., 2006. Leave-one-out cross-validation based model selection criteria for weighted LS-SVMs. Proceedings of the 2006 IEEE International Joint Conference on Neural Network Proceedings, July 16-21, 2006, IEEE, Norwich, England, ISBN:0-7803-9490-9, pp: 1661-1668.

Chen, X., O. Linton and V.I. Keilegom, 2003. Estimation of semiparametric models when the criterion function is not smooth. Econometrica, 71: 1591-1608.
CrossRef  |  Direct Link  |  

Dash, P. and M. Nayak, 2013. A study on principal component analysis for lossless data compression. Indian J. Res., 2: 125-129.

Delaigle, A. and P. Hall, 2012. Achieving near perfect classification for functional data. J. R. Stat. Soc. Ser. B., 74: 267-286.
CrossRef  |  Direct Link  |  

Dobbin, K.K., Y. Zhao and R.M. Simon, 2008. How large a training set is needed to develop a classifier for microarray data?. Clin. Cancer Res., 14: 108-114.

Engle, R.F. and S. Manganelli, 2004. Quantile Prediction. In: Economic Forecasting, Elliott, G. and A. Timmermann (Eds.). Elsevier, Netherlands, pp: 964-968.

Hastie, T., R. Tibshirani and J. Friedman, 2009. The Elements of Statistical Learning: Data Mining, Inference and Prediction. 2nd Edn., Springer, New York, pp: 520-528.

Higgins, J.J., 2004. An Introduction to Modern Nonparametric Statistics. Brooks/Cole, California, USA., ISBN:9780534387754, Pages: 366.

Kim, T.W., 2003. Nonparametric approaches for drought characterization and forecasting. BA Thesis, The University of Arizona, Tucson, Arizona. http://arizona.openrepository.com/arizona/handle/10150/280424

Kyperountas, M., A. Tefas and I. Pitas, 2005. Methods for improving discriminant analysis for face authentication. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'05), March 23, 2005, IEEE, Thessaloniki, Greece, ISBN:0-7803-8874-7, pp: 549-549.

Labib, K. and V.R. Vemuri, 2006. An application of principal component analysis to the detection and visualization of computer network attacks. Ann. Telecommun., 61: 218-234.
CrossRef  |  Direct Link  |  

Lee, Y., 2010. Support Vector Machines for Classification: A Statistical Portrait. In: Statistical Methods in Molecular Biology, Heejung, B., X.Z. Kathy, L.V.E. Heather and M. Mazumdar (Eds.). Springer, Berlin, Germany, ISBN:978-1-60761-578-1, pp: 347-368.

Lu, Z. and Y. Zhang, 2012. An augmented lagrangian approach for sparse principal component analysis. Math. Program., 135: 149-193.
CrossRef  |  Direct Link  |  

Mason, R. and W.G. Brown, 1975. Multicollinearity problems and ridge regression in sociological models. Soc. Sci. Res., 4: 135-149.
CrossRef  |  Direct Link  |  

Neideen, T. and K. Brasel, 2007. Understanding statistical tests. J. Surg. Educ., 64: 93-96.

Okwonu, F.Z. and A.R. Othman, 2012. A model classification technique for linear discriminant analysis for two groups. Intl. J. Comput. Sci. Issues, 1: 125-128.

Qiao, Z., L. Zhou and J.Z. Huang, 2008. Effective linear discriminant analysis for high dimensional, low sample size data. Proceeding of the World Congress on Engineering, July 2-4, 2008, Texas A&M University, Cambridge, Massachusetts, ISBN:978-988-17012-3-7, pp: 2-4.

Samaneh, M.I., A. Khosro and Z. Leyla, 2016. Bayesian variable selection under collinearity of parameters. Res. J. Appl. Sci., 11: 428-438.

Sheskin, D., 2004. Handbook of Parametric and Nonparametric Statistical Procedures. 3rd Ed., Chapman and Hall /CRC, Boca Raton, ISBN: 9781584884408, Pages: 1193.

Shlens, J., 2014. A tutorial on principal component analysis. arXiv Preprint arXiv:1404.1100, 1: 1-12.
Direct Link  |  

Voss, D.S., 2004. Multicollinearity. Encycl. Soc. Meas., 2: 759-770.

Yu, Y., 2012. Bayesian and non-parametric approaches to missing data analysis. Ph.D Thesis, University of California, California, USA., https://escholarship.org/uc/item/3378b6tx