Hui Zou

About

Hui Zou is a professor at the University of Minnesota's School of Statistics. Her research focuses on statistical methodology and theory, contributing to the development of advanced statistical techniques and their applications. She is involved in the study of statistical inference, methodology, and theory, with a particular emphasis on statistical methodology and theory.

Research topics

• Computer Science
• Artificial Intelligence
• Data science
• Algorithm
• Mathematical optimization
• Applied mathematics
• Mathematics

Selected publications

• An Alternating Manifold Proximal Gradient Method for Sparse Principal Component Analysis and Sparse Canonical Correlation Analysis

  INFORMS Journal on Optimization · 2020 · 28 citations

  Senior authorCorresponding

  Sparse principal component analysis and sparse canonical correlation analysis are two essential techniques from high-dimensional statistics and machine learning for analyzing large-scale data. Both problems can be formulated as an optimization problem with nonsmooth objective and nonconvex constraints. Because nonsmoothness and nonconvexity bring numerical difficulties, most algorithms suggested in the literature either solve some relaxations of them or are heuristic and lack convergence guarant…

  DOI

• Enveloped Huber Regression

  Journal of the American Statistical Association · 2023-11-06 · 10 citations

  articleSenior authorCorresponding

  Huber regression (HR) is a popular flexible alternative to the least squares regression when the error follows a heavy-tailed distribution. We propose a new method called the enveloped Huber regression (EHR) by considering the envelope assumption that there exists some subspace of the predictors that has no association with the response, which is referred to as the immaterial part. More efficient estimation is achieved via the removal of the immaterial part. Different from the envelope least squ…

  PublisherDOI

• L1 Regularization for High-Dimensional Multivariate GARCH Models

  Risks · 2024-02-04 · 3 citations

  articleOpen access

  The complexity of estimating multivariate GARCH models increases significantly with the increase in the number of asset series. To address this issue, we propose a general regularization framework for high-dimensional GARCH models with BEKK representations, and obtain a penalized quasi-maximum likelihood (PQML) estimator. Under some regularity conditions, we establish some theoretical properties, such as the sparsity and the consistency, of the PQML estimator for the BEKK representations. We the…

  PublisherOA PDFDOI

• Minimax Optimal Rates With Heavily Imbalanced Binary Data

  IEEE Transactions on Information Theory · 2024-09-12 · 1 citations

  articleSenior author

  In a wide range of binary prediction and estimation tasks, the data set exhibits a high degree of imbalance between the sample sizes of the two classes, which greatly hinders the performance of standard machine learning methods. In spite of a vast collection of methods aiming to achieve better performance on heavily imbalanced data, the theoretical limit of estimation with imbalanced data remains unknown. This paper provides some insights into the imbalanced classification problem by establishin…

  PublisherDOI

• Tensor mixture discriminant analysis with applications to sensor array data analysis

  The Annals of Applied Statistics · 2024-01-31 · 1 citations

  articleSenior author

  Sensor arrays are often used to identify chemicals by measuring properly chosen chemical interactions. Machine learning techniques are of vital importance to accurately recognize a chemical based on the sensor array measurements. However, sensor array data often take the form of matrices (i.e, two-way tensors), and the concentration levels may have a complex impact on the measurements. Hence, existing linear and/or vector classification methods may be inadequate for sensor array data. In this ar…

  PublisherDOI

Recent grants

• Flexible Statistical Modelling for High Dimensional Data

  NSF · $180k · 2019–2023

• Collaborative Research: New Statistical Methods and Theory for High-Dimensional Data

  NSF · $174k · 2015–2018

• Novel Inference Procedures for Non-Standard High-Dimensional Regression Models

  NSF · $150k · 2020–2023

Frequent coauthors

• Lingzhou Xue

  38 shared

• Jianqing Fan

  36 shared

• Ping Zhong

  China Agricultural University

  29 shared

• Runze Li

  20 shared

• Zhencai Shen

  China Agricultural University

  19 shared

• Yingyi Chen

  18 shared

• Cun-Hui Zhang

  16 shared

• Qing Mai

  14 shared

Awards & honors

• Fellow, American Statistical Association, 2019
• Fellow of Institute of Mathematical Statistics, 2015
• Web of Science Highly Cited Researcher, 2014 - 2019
• Cogs Outstanding Faculty Award, 2013
• IMS Tweedie Award 2011

Similar researchers at University of Minnesota

• Mingyi Hongh-56
• L. Jeff Hongh-49
• Shuzhong Zhangh-49