64 citations · 192 across the 29 of their papers we have counts for
7 papers · 1 filter
Neural Gaussian Mirror for Controlled Feature Selection in Neural Networks
Xin Xing, Yu Gui, Chenguang Dai +1
Deep neural networks (DNNs) have become increasingly popular and achieved outstanding performance in predictive tasks. However, the DNN framework itself cannot inform the user whic…
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…
Measurement error models: from nonparametric methods to deep neural networks
Zhirui Hu, Zheng Tracy Ke, Jun S Liu
The success of deep learning has inspired recent interests in applying neural networks in statistical inference. In this paper, we investigate the use of deep neural networks for n…
A Scale-free Approach for False Discovery Rate Control in Generalized Linear Models
Chenguang Dai, Buyu Lin, Xin Xing +1
The generalized linear models (GLM) have been widely used in practice to model non-Gaussian response variables. When the number of explanatory features is relatively large, scienti…
Bayesian Bi-clustering Methods with Applications in Computational Biology
Han Yan, Jiexing Wu, Yang Li +1
Bi-clustering is a useful approach in analyzing biological data when observations come from heterogeneous groups and have a large number of features. We outline a general Bayesian…
Generative Multiple-purpose Sampler for Weighted M-estimation
Minsuk Shin, Shijie Wang, Jun S Liu
To overcome the computational bottleneck of various data perturbation procedures such as the bootstrap and cross validations, we propose the Generative Multiple-purpose Sampler (GM…
False Discovery Rate Control via Data Splitting
Chenguang Dai, Buyu Lin, Xin Xing +1
Selecting relevant features associated with a given response variable is an important issue in many scientific fields. Quantifying quality and uncertainty of a selection result via…