References
- Arashi, M., Tabatabaey, S. M. M., Hassanzadeh Bashtian, M. (2014). Shrinkage Ridge Estimators in Linear Regression. Communications in Statistics: Simulation and Computation 43:871–904.
- Firinguetti, L., Bobadilla, G. (2011). Asymptotic confidence intervals in ridge regression based on the Edgeworth expansion. Statistical Papers 52:287–307.
- Frank, I. E., Friedman, J. H. (1993). A statistical view of chemometrics regression tools. Technometrics 35:109–148.
- Garthwaite, P. H. (1994). An interpretation of partial least squares. Journal of the American Statistical Association 89:122–127.
- Geladi, P., Kowlaski, B. (1986). Partial least squares regression: A tutorial. Analytica Chemica Acta 35:1–17.
- Goutis, C. (1996). Partial least squares algorithm yields shrinkage estimators. The Annals of Statistics 24:816–824.
- Hald, A. (1952). Statistical Theory with Engineering Applications. New York: Wiley.
- Helland, I. (1988). On the structure of partial least squares regression. Communications in Statistics, Simulation and Computation 17:581–607.
- Hoerl, A. E., Kennard, R. W. (1970a). Ridge regression: Biased estimation for nonorthogonal problems. Technometrics 12:55–67.
- Hoerl, A. E., Kennard, R. W. (1970b). Ridge regression: Applications to nonorthogonal problems. Technometrics 12:69–82.
- Höskuldson, A. (1988). PLS regression methods. Journal of Chemometrics 2:211–228.
- Khalaf, G., Månsson, K., Shukur, G. (2013). Modified ridge regression estimators. Communications in Statistics: Theory and Methods 42:1476–1487.
- Kibria, B. M. G. (2003). Performance of some new ridge regression estimators. Communications in Statistics: Simulation and Computation 32:419–435.
- Kibria, B. M. G. (2004). Performance of the shrinkage preliminary test ridge regression estimators based on the conflicting of W, LR and LM tests. Journal of Statistical Computation and Simulation 74:793–810.
- Kibria, B. M. G. (2012). Some Liu and ridge-type estimators and their properties under the ill-conditioned Gaussian linear regression model. Journal of Statistical Computation and Simulation 82:1–17.
- Krämer, N. (2007). An overview on the shrinkage properties of partial least squares regression. Computational Statistics 22:249–273.
- Lawless, J., Wang, P. (1976). A simulation study of ridge regression and other regression estimators. Communications in Statistics: Theory and Methods 5:1615–1624.
- Mansson, K., Ghazi, S., Kibria, B. M. G. (2010). On some ridge regression estimators: A Monte Carlo simulation study under different error variances. Journal of Statistics 17:1–22.
- Muniz, G., Kibria, B. M. G. (2009). On some ridge regression estimators: An empirical comparisons. Communications in Statistics: Simulation and Computation 38:621–630.
- Muniz, G., Kibria, B. M. G., Mansson, K., Shukur, G. (2012). On developing ridge regression parameters: A graphical investigation. SORT 36:115–138.
- Najarian, S., Arashi, M., Kibria, B. M. G. (2013). A simulation study on some restricted ridge regression estimators. Communications in Statistics: Simulation and Computation 42:871–890.
- Phatak, A., De Jong, F. (1997). The geometry of partial least squares. Journal of Chenometrics 11:331–338.
- Wold, H. (1966). Estimation of principal components and related models by iterative least squares. In: Krishnaiaah, P. R., ed., Multivariate Analysis. New York: Academic Press, pp. 391–420.
- Wold, S., Ruhe, A., Wold, H., Dunn, III, W. (1984). The collinearity problem in linear regression. The partial least squares (PLS) approach to generalized inverses. SIAM Journal of Scientific Statistical Computing 5:735–744.