10 citations · 12 across the 5 of their papers we have counts for
6 papers
Linear Functional Testing with General Loadings in Sparse Regression: Separation Rates and Computational Barriers
Jie Xie, Dongming Huang
We study the problem of testing in high-dimensional sparse linear regression with Gaussian random design and unknown design covariance. The loading vector is…
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…
On the Structural Dimension of Sliced Inverse Regression
Dongming Huang, Songtao Tian, Qian Lin
In this work, we address the longstanding puzzle that Sliced Inverse Regression (SIR) often performs poorly for sufficient dimension reduction when the structural dimension (th…
Catalytic Priors: Using Synthetic Data to Specify Prior Distributions in Bayesian Analysis
Dongming Huang, Feicheng Wang, Donald B. Rubin +1
Catalytic prior distributions provide general, easy-to-use, and interpretable specifications of prior distributions for Bayesian analysis. They are particularly beneficial when the…
Relaxing the Assumptions of Knockoffs by Conditioning
Dongming Huang, Lucas Janson
The recent paper Candès et al. (2018) introduced model-X knockoffs, a method for variable selection that provably and non-asymptotically controls the false discovery rate with no r…
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 …