1.5k citations · 1.7k across the 34 of their papers we have counts for
8 papers · 1 filter
High Dimensional Semiparametric Scale-Invariant Principal Component Analysis
Fang Han, Han Liu
We propose a new high dimensional semiparametric principal component analysis (PCA) method, named Copula Component Analysis (COCA). The semiparametric model assumes that, after uns…
Joint Estimation of Multiple Graphical Models from High Dimensional Time Series
Huitong Qiu, Fang Han, Han Liu +1
In this manuscript we consider the problem of jointly estimating multiple graphical models in high dimensions. We assume that the data are collected from n subjects, each of which…
ECA: High Dimensional Elliptical Component Analysis in non-Gaussian Distributions
Fang Han, Han Liu
We present a robust alternative to principal component analysis (PCA) --- called elliptical component analysis (ECA) --- for analyzing high dimensional, elliptically distributed da…
Challenges of Big Data Analysis
Jianqing Fan, Fang Han, Han Liu
Big Data bring new opportunities to modern society and challenges to data scientists. On one hand, Big Data hold great promises for discovering subtle population patterns and heter…
A Direct Estimation of High Dimensional Stationary Vector Autoregressions
Fang Han, Huanran Lu, Han Liu
The vector autoregressive (VAR) model is a powerful tool in modeling complex time series and has been exploited in many fields. However, fitting high dimensional VAR model poses so…
Sparse Principal Component Analysis for High Dimensional Vector Autoregressive Models
Zhaoran Wang, Fang Han, Han Liu
We study sparse principal component analysis for high dimensional vector autoregressive time series under a doubly asymptotic framework, which allows the dimension to scale wit…
Statistical analysis of latent generalized correlation matrix estimation in transelliptical distribution
Fang Han, Han Liu
Correlation matrices play a key role in many multivariate methods (e.g., graphical model estimation and factor analysis). The current state-of-the-art in estimating large correlati…
High Dimensional Semiparametric Gaussian Copula Graphical Models
Han Liu, Fang Han, Ming Yuan +2
In this paper, we propose a semiparametric approach, named nonparanormal skeptic, for efficiently and robustly estimating high dimensional undirected graphical models. To achieve m…