1.5k citations · 3.2k across the 70 of their papers we have counts for
13 papers · 1 filter
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…
R-estimators in GARCH models; asymptotics, applications and bootstrapping
Hang Liu, Kanchan Mukherjee
The quasi-maximum likelihood estimation is a commonly-used method for estimating GARCH parameters. However, such estimators are sensitive to outliers and their asymptotic normality…
High-Temperature Structure Detection in Ferromagnets
Yuan Cao, Matey Neykov, Han Liu
This paper studies structure detection problems in high temperature ferromagnetic (positive interaction only) Ising models. The goal is to distinguish whether the underlying graph…
A Unified Framework for Testing High Dimensional Parameters: A Data-Adaptive Approach
Cheng Zhou, Xinsheng Zhang, Wenxin Zhou +1
High dimensional hypothesis test deals with models in which the number of parameters is significantly larger than the sample size. Existing literature develops a variety of individ…
A New Perspective on Robust -Estimation: Finite Sample Theory and Applications to Dependence-Adjusted Multiple Testing
Wen-Xin Zhou, Koushiki Bose, Jianqing Fan +1
Heavy-tailed errors impair the accuracy of the least squares estimate, which can be spoiled by a single grossly outlying observation. As argued in the seminal work of Peter Huber i…
Property Testing in High Dimensional Ising models
Matey Neykov, Han Liu
This paper explores the information-theoretic limitations of graph property testing in zero-field Ising models. Instead of learning the entire graph structure, sometimes testing a…
Adaptive Inferential Method for Monotone Graph Invariants
Junwei Lu, Matey Neykov, Han Liu
We consider the problem of undirected graphical model inference. In many applications, instead of perfectly recovering the unknown graph structure, a more realistic goal is to infe…
Combinatorial Inference for Graphical Models
Matey Neykov, Junwei Lu, Han Liu
We propose a new family of combinatorial inference problems for graphical models. Unlike classical statistical inference where the main interest is point estimation or parameter te…
Nonparametric Heterogeneity Testing For Massive Data
Junwei Lu, Guang Cheng, Han Liu
A massive dataset often consists of a growing number of (potentially) heterogeneous sub-populations. This paper is concerned about testing various forms of heterogeneity arising fr…
I-LAMM for Sparse Learning: Simultaneous Control of Algorithmic Complexity and Statistical Error
Jianqing Fan, Han Liu, Qiang Sun +1
We propose a computational framework named iterative local adaptive majorize-minimization (I-LAMM) to simultaneously control algorithmic complexity and statistical error when fitti…
Robust Inference of Risks of Large Portfolios
Jianqing Fan, Fang Han, Han Liu +1
We propose a bootstrap-based robust high-confidence level upper bound (Robust H-CLUB) for assessing the risks of large portfolios. The proposed approach exploits rank-based and qua…
A Partially Linear Framework for Massive Heterogeneous Data
Tianqi Zhao, Guang Cheng, Han Liu
We consider a partially linear framework for modelling massive heterogeneous data. The major goal is to extract common features across all sub-populations while exploring heterogen…
Some Two-Step Procedures for Variable Selection in High-Dimensional Linear Regression
Jian Zhang, Xinge Jessie Jeng, Han Liu
We study the problem of high-dimensional variable selection via some two-step procedures. First we show that given some good initial estimator which is -consistent b…