1.5k citations · 3.2k across the 70 of their papers we have counts for
74 papers
Sparse PCA with Oracle Property
Quanquan Gu, Zhaoran Wang, Han Liu
In this paper, we study the estimation of the -dimensional sparse principal subspace of covariance matrix in the high-dimensional setting. We aim to recover the oracle princ…
Optimal Covariate Balancing Conditions in Propensity Score Estimation
Jianqing Fan, Kosuke Imai, Inbeom Lee +3
Inverse probability of treatment weighting (IPTW) is a popular method for estimating the average treatment effect (ATE). However, empirical studies show that the IPTW estimators ca…
Rank-Based Testing for Semiparametric VAR Models: a measure transportation approach
Marc Hallin, Davide La Vecchia, Hang Liu
We develop a class of tests for semiparametric vector autoregressive (VAR) models with unspecified innovation densities, based on the recent measure-transportation-based concepts o…
The flare Package for High Dimensional Linear Regression and Precision Matrix Estimation in R
Xingguo Li, Tuo Zhao, Xiaoming Yuan +1
This paper describes an R package named flare, which implements a family of new high dimensional regression methods (LAD Lasso, SQRT Lasso, Lasso, and Dantzig selector) an…
Picasso: A Sparse Learning Library for High Dimensional Data Analysis in R and Python
Jason Ge, Xingguo Li, Haoming Jiang +4
We describe a new library named picasso, which implements a unified framework of pathwise coordinate optimization for a variety of sparse learning problems (e.g., sparse linear reg…
The huge Package for High-dimensional Undirected Graph Estimation in R
Tuo Zhao, Han Liu, Kathryn Roeder +2
We describe an R package named huge which provides easy-to-use functions for estimating high dimensional undirected graphs from data. This package implements recent results in the…