64 citations · 192 across the 29 of their papers we have counts for
7 papers · 1 filter
On the Optimality of Functional Sliced Inverse Regression
Rui Chen, Songtao Tian, Dongming Huang +2
In this paper, we prove that functional sliced inverse regression (FSIR) achieves the optimal (minimax) rate for estimating the central space in functional sufficient dimension red…
Power of Knockoff: The Impact of Ranking Algorithm, Augmented Design, and Symmetric Statistic
Zheng Tracy Ke, Jun S. Liu, Yucong Ma
The knockoff filter is a recent false discovery rate (FDR) control method for high-dimensional linear models. We point out that knockoff has three key components: ranking algorithm…
Global testing under the sparse alternatives for single index models
Qian Lin, Zhigen Zhao, Jun S. Liu
For the single index model with Gaussian design, %satisfying that rank where is unknown and is a sparse -dimensional unit vect…
On the optimality of sliced inverse regression in high dimensions
Qian Lin, Xinran Li, Dongming Huang +1
The central subspace of a pair of random variables is the minimal subspace such that …
Sparse Sliced Inverse Regression Via Lasso
Qian Lin, Zhigen Zhao, Jun S. Liu
For multiple index models, it has recently been shown that the sliced inverse regression (SIR) is consistent for estimating the sufficient dimension reduction (SDR) space if and on…
Signed Support Recovery for Single Index Models in High-Dimensions
Matey Neykov, Qian Lin, Jun S. Liu
In this paper we study the support recovery problem for single index models , where is an unknown link function, $\bol…
On consistency and sparsity for sliced inverse regression in high dimensions
Qian Lin, Zhigen Zhao, Jun S. Liu
We provide here a framework to analyze the phase transition phenomenon of slice inverse regression (SIR), a supervised dimension reduction technique introduced by \cite{Li:1991}. U…