183 citations · 469 across the 31 of their papers we have counts for
7 papers · 1 filter
On the Finite-Sample Analysis of -estimators
Yiyuan She
In large-scale modern data analysis, first-order optimization methods are usually favored to obtain sparse estimators in high dimensions. This paper performs theoretical analysis o…
Robust Reduced Rank Regression
Yiyuan She, Kun Chen
In high-dimensional multivariate regression problems, enforcing low rank in the coefficient matrix offers effective dimension reduction, which greatly facilitates parameter estimat…
Group Regularized Estimation under Structural Hierarchy
Yiyuan She, Zhifeng Wang, He Jiang
Variable selection for models including interactions between explanatory variables often needs to obey certain hierarchical constraints. The weak or strong structural hierarchy req…
The Group Square-Root Lasso: Theoretical Properties and Fast Algorithms
Florentina Bunea, Johannes Lederer, Yiyuan She
We introduce and study the Group Square-Root Lasso (GSRL) method for estimation in high dimensional sparse regression models with group structure. The new estimator minimizes the s…
Joint variable and rank selection for parsimonious estimation of high-dimensional matrices
Florentina Bunea, Yiyuan She, Marten H. Wegkamp
We propose dimension reduction methods for sparse, high-dimensional multivariate response regression models. Both the number of responses and that of the predictors may exceed the…
Optimal selection of reduced rank estimators of high-dimensional matrices
Florentina Bunea, Yiyuan She, Marten H. Wegkamp
We introduce a new criterion, the Rank Selection Criterion (RSC), for selecting the optimal reduced rank estimator of the coefficient matrix in multivariate response regression mod…
Thresholding-based Iterative Selection Procedures for Model Selection and Shrinkage
Yiyuan She
This paper discusses a class of thresholding-based iterative selection procedures (TISP) for model selection and shrinkage. People have long before noticed the weakness of the conv…