5 citations · 11 across the 7 of their papers we have counts for
7 papers
Testing Independence of Bivariate Censored Data using Random Walk on Restricted Permutation Graph
Seonghun Cho, Donghyeon Yu, Johan Lim
In this paper, we propose a procedure to test the independence of bivariate censored data, which is generic and applicable to any censoring types in the literature. To test the hyp…
An efficient GPU-Parallel Coordinate Descent Algorithm for Sparse Precision Matrix Estimation via Scaled Lasso
Seunghwan Lee, Sang Cheol Kim, Donghyeon Yu
The sparse precision matrix plays an essential role in the Gaussian graphical model since a zero off-diagonal element indicates conditional independence of the corresponding two va…
An efficient parallel block coordinate descent algorithm for large-scale precision matrix estimation using graphics processing units
Young-Geun Choi, Seunghwan Lee, Donghyeon Yu
Large-scale sparse precision matrix estimation has attracted wide interest from the statistics community. The convex partial correlation selection method (CONCORD) developed by Kha…
Easily parallelizable and distributable class of algorithms for structured sparsity, with optimal acceleration
Seyoon Ko, Donghyeon Yu, Joong-Ho Won
Many statistical learning problems can be posed as minimization of a sum of two convex functions, one typically a composition of non-smooth and linear functions. Examples include r…
High-dimensional Fused Lasso Regression using Majorization-Minimization and Parallel Processing
Donghyeon Yu, Joong-Ho Won, Taehoon Lee +2
In this paper, we propose a majorization-minimization (MM) algorithm for high-dimensional fused lasso regression (FLR) suitable for parallelization using graphics processing units…
Monotone false discovery rate
Joong-Ho Won, Johan Lim, Donghyeon Yu +2
This paper proposes a procedure to obtain monotone estimates of both the local and the tail false discovery rates that arise in large-scale multiple testing. The proposed monotoniz…
Regression shrinkage and grouping of highly correlated predictors with HORSES
Woncheol Jang, Johan Lim, Nicole A. Lazar +2
Identifying homogeneous subgroups of variables can be challenging in high dimensional data analysis with highly correlated predictors. We propose a new method called Hexagonal Oper…