64 citations · 96 across the 8 of their papers we have counts for
6 papers · 1 filter
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…
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…
Controlling False Discovery Rate Using Gaussian Mirrors
Xin Xing, Zhigen Zhao, Jun S. Liu
Simultaneously finding multiple influential variables and controlling the false discovery rate (FDR) for linear regression models is a fundamental problem. We here propose the Gaus…
Minimax Nonparametric Two-sample Test under Smoothing
Xin Xing, Zuofeng Shang, Pang Du +3
We consider the problem of comparing probability densities between two groups. A new probabilistic tensor product smoothing spline framework is developed to model the joint density…
Lookahead Strategies for Sequential Monte Carlo
Ming Lin, Rong Chen, Jun S. Liu
Based on the principles of importance sampling and resampling, sequential Monte Carlo (SMC) encompasses a large set of powerful techniques dealing with complex stochastic dynamic s…
The EM Algorithm and the Rise of Computational Biology
Xiaodan Fan, Yuan Yuan, Jun S. Liu
In the past decade computational biology has grown from a cottage industry with a handful of researchers to an attractive interdisciplinary field, catching the attention and imagin…