An improvement method of sliced inverse regression using principal component analysis

We suggest an improvement method of sliced inverse regression (SIR) which is called gPCA-SIR h. The idea of the method is re-separating the group of the data, which is separated in some slices of the response variable, by using principal components. We also compare SIR with PCA-SIR by simulation, and prove the convergence of estimator.