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20182023
most citedSparse Laplacian Shrinkage with the Graphical Lasso Estimator for Regression Problems

1 citations · 1 across the 4 of their papers we have counts for

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6 papers · 1 filter

stat.ME2023

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…

stat.ME2022

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…

stat.ME2020

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…

stat.ME2019★ 1 cited

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…

stat.ME2019

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…

stat.ME2018

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…