5 citations · 17 across the 13 of their papers we have counts for
13 papers
A model-free feature selection technique of feature screening and random forest based recursive feature elimination
Siwei Xia, Yuehan Yang
In this paper, we propose a model-free feature selection method for ultra-high dimensional data with mass features. This is a two phases procedure that we propose to use the fused…
Dimension reduction of high-dimension categorical data with two or multiple responses considering interactions between responses
Yuehan Yang
This paper models categorical data with two or multiple responses, focusing on the interactions between responses. We propose an efficient iterative procedure based on sufficient d…
A sequential stepwise screening procedure for sparse recovery in high-dimensional multiresponse models with complex group structures
Weixiong Liang, Yuehan Yang
Multiresponse data with complex group structures in both responses and predictors arises in many fields, yet, due to the difficulty in identifying complex group structures, only a…
Design-based theory for Lasso adjustment in randomized block experiments and rerandomized experiments
Ke Zhu, Hanzhong Liu, Yuehan Yang
Blocking, a special case of rerandomization, is routinely implemented in the design stage of randomized experiments to balance the baseline covariates. This study proposes a regres…
Randomization-based joint central limit theorem and efficient covariate adjustment in stratified factorial experiments
Hanzhong Liu, Jiyang Ren, Yuehan Yang
Randomized block factorial experiments are widely used in industrial engineering, clinical trials, and social science. Researchers often use a linear model and analysis of covarian…
Interaction Pursuit Biconvex Optimization
Yuehan Yang, Siwei Xia, Hu Yang
Multivariate regression models are widely used in various fields such as biology and finance. In this paper, we focus on two key challenges: (a) When should we favor a multivariate…
Regression-adjusted average treatment effect estimates in stratified randomized experiments
Hanzhong Liu, Yuehan Yang
Researchers often use linear regression to analyse randomized experiments to improve treatment effect estimation by adjusting for imbalances of covariates in the treatment and cont…
Sparse Laplacian Shrinkage with the Graphical Lasso Estimator for Regression Problems
Yuehan Yang, Siwei Xia, Hu Yang
This paper considers a high-dimensional linear regression problem where there are complex correlation structures among predictors. We propose a graph-constrained regularization pro…
Smooth Adjustment for Correlated Effects
Yuehan Yang, Hu Yang
This paper considers a high dimensional linear regression model with corrected variables. A variety of methods have been developed in recent years, yet it is still challenging to k…
MSP: A Multi-step Screening Procedure for Sparse Recovery
Yuehan Yang, Ji Zhu, Edward I. George
We propose a Multi-step Screening Procedure (MSP) for the recovery of sparse linear models in high-dimensional data. This method is based on a repeated small penalty strategy that…
Penalized regression adjusted causal effect estimates in high dimensional randomized experiments
Hanzhong Liu, Yuehan Yang
Regression adjustments are often considered by investigators to improve the estimation efficiency of causal effect in randomized experiments when there exists many pre-experiment c…
Model Selection Consistency of Lasso for Empirical Data
Yuehan Yang, Hu Yang
Large-scale empirical data, the sample size and the dimension are high, often exhibit various characteristics. For example, the noise term follows unknown distributions or the mode…
Adaptive elastic net and Separate Selection from Least Squares for ultra-high dimensional regression models
Yuehan Yang, Hu Yang
This paper studies the asymptotic properties of the adaptive elastic net in ultra-high dimensional sparse linear regression models and proposes a new method called SSLS (Separate S…