Sparse Sliced Inverse Regression via Random Projection

05/09/2023
by   Jia Zhang, et al.
0

We propose a novel sparse sliced inverse regression method based on random projections in a large p small n setting. Embedded in a generalized eigenvalue framework, the proposed approach finally reduces to parallel execution of low-dimensional (generalized) eigenvalue decompositions, which facilitates high computational efficiency. Theoretically, we prove that this method achieves the minimax optimal rate of convergence under suitable assumptions. Furthermore, our algorithm involves a delicate reweighting scheme, which can significantly enhance the identifiability of the active set of covariates. Extensive numerical studies demonstrate high superiority of the proposed algorithm in comparison to competing methods.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset