lasso estimates can be obtained at the same computational cost as that of an ordinary least squares estimation Hastie et al. (òýýÀ). Further, the lasso estimator remains numerically feasible for dimensions m that are much higher than the sample size n. Zou and Hastie (òýý ) introduced a hybrid PLS regression with the so called elastic net penalty de ned as "Ppj =Ô( ò ( j + Ô − )S jS). Here the penalty function is a linear combination of the ridge regression penalty function and lasso penalty function. A di erent type of PLS, called garotte is due to Breiman (ÔÀÀç). Further, PLS estimation provides a generalization of both nonparametric least squares and weighted projection estimators, and a.
