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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…
Sparse Median Graphs Estimation in a High Dimensional Semiparametric Model
Fang Han, Han Liu, Brian Caffo
In this manuscript a unified framework for conducting inference on complex aggregated data in high dimensional settings is proposed. The data are assumed to be a collection of mult…
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…