General

1. Anderson, T.W. (2003) An Introduction to Multivariate Statistical Analysis. 3^th  Ed.. New York: Wiley. 

2. Flury, B. (1997) A First Course in Multivariate Statistics. New York: Springer-Verlag.

3. Krzanowski, W.J. (2000) Principles of Multivariate Analysis. Revised Ed.. Oxford: Oxford University Press.

4. Mardia, K.V.,  Kent, J.T. and Bibby J.M. (1979) Multivariate Analysis. London: Academic Press.

5. Muirhead, R.J. (1982) Aspects of Multivariate Statistical Theory. New York: Wiley.

6. Peña, D. (2002) Análisis de Datos Multivariantes. McGraw-Hill.

7. Rencher, A.C. (1992) “Interpretation of canonical discriminant functions, canonical variates and principal components.” The American Statistician, 46, 217-225.

8. Rencher, A.C. (1995) Methods of Multivariate Analysis. New York: Wiley.

9. Rencher, A.C. (1998) Multivariate Statistical Inference and Applications. New York: Wiley.

10. Schervish, M.J. (1987) “A review of multivariate analysis.” Statist. Sci., 2, 396-433.
     

Aspectos Computacionales y Aplicaciones

1. Afifi, A.A. and Clark, V. (2004) Computer-aided Multivariate Analysis, 4/ed.. London: Chapman and Hall/CRC.

2. Everitt, B. (2005) An R and S-PLUS® Companion to Multivariate Analysis. Springer-Verlag.
    

Nuevas Perspectivas

1. Hastie, T., Tibshirani, R. and Friedman, J. (2009) The Elements of Statistical Learning: Data Mining, Inference and Prediction. 2^th  Ed.. New York: Springer.

2. Izenman, A.J. (2008) Modern multivariate statistical techniques: regression, classification, and manifold learning. New York: Springer.

3. James, G., Witten, D., Hastie, T., Tibshirani, R. (2013) An Introduction to Statistical Learning with Applications in R.  New York: Springer.