Using the Bootstrap to Select One of a New Class of Dimension Reduction Methods
利用自助法选择一类新的降维方法
Dimension reduction in a regression analysis of response y given a p-dimensional vector of predictors x reduces the dimension of x by replacing it with a lower-dimensional linear combination β′x of the x's without specifying a parametric model and without loss of information about the conditional distribution of y given x. We unify three existing methods, sliced inverse regression (SIR), sliced average variance estimate (SAVE), and principal Hessian directions (pHd), into a…