The Statistics Program at UNC was founded by Harold Hotelling in 1946 and is one of the oldest statistics programs in the country. The Statistics Group plays a central role in the department’s research mission; statistics faculty are active inside and outside the University.
The interests and expertise of faculty in the Statistics Group cover numerous active areas of statistical research, including inference for complex and multi-view data, machine learning, data science, deep learning, network analysis, environmental statistics, statistical genomics,
time series, dynamical systems, and financial statistics. Group members are engaged in basic theory, the development of new statistical methods, and the creation of publicly available statistical software. In many cases, methodological development is carried out in close collaboration with researchers in a number of fields, including genetics, genomics, medical imaging, biology, biomedicine, social sciences, and bioinformatics.
Members of the Statistics Group are active in professional organizations and the editorial boards of numerous statistical journals. Their research is supported by a variety of sources, including grants from the National Science Foundation and the National Institutes of Health.
Theory and Methodology
- Inference for high-dimensional data
- Machine learning and data mining
- Adversarial deep learning
- Statistical analysis of networks
- Spatial statistics
- Inference for time series and dynamical systems
- Fiducial inference
- Object oriented data analysis
- Neuroscience and brain connectomics
- Personalized medicine
- Medical imaging
Members of the Statistics Group and their students are engaged in a wide variety of collaborations with disciplinary researchers at UNC and elsewhere. Below is a partial list of researchers at UNC with whom members of the Statistics Group have had recent or have ongoing collaborations.
- Dirk Dittmer (Microbiology and Immunology)
- Katherine Hoadley (Lineberger Cancer Center)
- Corbin Jones (Biology)
- Michael Kosorok (Biostatistics)
- Amanda E. Nelson (School of Medicine)
- Marc Niethammer (Computer Science)
- Charles M. Perou (Lineberger Cancer Center and Genetics)
- Stephen M. Pizer (Computer Science)
- Nancy E. Thomas (School of Medicine)
Special Topics Courses
- Statistical Computing (Zhang)
- Object Oriented Data Analysis (Marron)
- Introduction to Computational Finance (Ji)
- Nonparametric Statistics (Zhang)
- Extreme Value Theory (Smith)
- Monte Carlo Methods (Ji)
- Time Series and Multivariate Analysis (Pipiras)
- Statistical Methods for Climate Research (Smith)
- Statistical Machine Learning and Data Mining (Liu)
- Reading Classics: Topics in Foundations of Statistics (Hannig)
- Numerical Methods for Uncertainty Quantification (Smith)
- MCMC in statistics and optimization (Hannig)
Z. Zhang, X. Wang, L. Kong & H. Zhu
High-Dimensional Spatial Quantile Function-on-Scalar Regression
Journal of the American Statistical Association 1-37 (2021).
K. McGoff & A. B. Nobel
Empirical Risk Minimization and Complexity of Dynamical Models
Annals of Statistics 48(4):2031-2054 (2020).
B. Liu, C. Zhou, X. Zhang & Y. Liu
A Unified Data-adaptive Framework for High Dimensional Change Point Detection
Journal of Royal Statistical Society, Series B 82(4): 933-963 (2020).
G. Yu, Q. Li, D. Shen & Y. Liu
Optimal Sparse Linear Prediction for Block-missing Multi-modality Data without Imputation
Journal of the American Statistical Association 15(531):1406-1419 (2020).
W. Mo, Z. Qi & Y. Liu
Learning Optimal Distributionally Robust Individualized Treatment Rules
Journal of the American Statistical Association to appear as a discussion paper (2020).
BET on Independence
Journal of the American Statistical Association: Theory and Methods 114(528):1620–1637 (2019).
G. Li, A. A. Shabalin, I. Rusyn, F. A. Wright & A. B. Nobel
An Empirical Bayes Approach for Multiple Tissue eQTL Analysis
Biostatistics 19(3):391-406 (2018).
K. Bodwin, K. Zhang & A. B. Nobel
A Testing-Based Approach to the Discovery of Differentially Correlated Variable Sets
Annals of Applied Statistics 12(2):1180-1203 (2018).
J. Livsey, R. Lund, S. Kechagias & V. Pipiras
Multivariate Integer-valued Time Series with Flexible Autocovariances and Their Application to Major Hurricane Counts
The Annals of Applied Statistics 12(1):408-431 (2018).
Q. Feng, J. Hannig, M. Jiang & J. S. Marron
Angle-Based Joint and Individual Variation Explained
Journal of Multivariate Analysis 166:241-265 (2018).
Z. Zhang, J. Su, E. Klassen, H. Le & A. Srivastava
Rate-invariant Analysis of Covariance Trajectories
Journal of Mathematical Imaging and Vision 60(8):1306-1323 (2018).
Y. Li, M. Cheng, K. Fujii, F. Hsieh & C. J. Hsieh
Learning from Group Comparisons: Exploiting Higher Order Interactions
NeurIPS 4986-4995 (2018).
J. Zhao, G. Yu & Y. Liu
Assessing Robustness of Classification Using an Angular Breakdown Point
Annals of Statistics 46(6B): 3362-3389 (2018).
C. Ji, T. Wang and L. Yin
Monte-Carlo Methods in Financial Modeling
Monte-Carlo Simulation-Based Statistical Modeling 285:317 (2017).
J. D. Wilson, J. Palowitch, S. Bhamidi & A. B. Nobel
Community extraction in multilayer networks with heterogeneous community structure
The Journal of Machine Learning Research 18(1):5458-5506 (2017).
C. Baek, R. A. Davis & V. Pipiras
Sparse Seasonal and Periodic Vector Autoregressive Modeling
Computational Statistics & Data Analysis 106:103-126 (2017).
J. Hannig, H. Iyer, R. C. S. Lai & T. C. M. Lee
Generalized Fiducial Inference: A Reviev and New Results
Journal of American Statistical Association 111(515):1346-1361 (2016).
J.D. Wilson, S. Wang, P. J. Mucha, S. Bhamidi & A. B. Nobel.
A Testing Based Extraction Algorithm for Identifying Significant Communities in Networks
Annals of Applied Statistics 8:1853-1891 (2014).
J. S. Marron & A. M. Alonso
Overview of Object Oriented Data Analysis
Biometrical Journal 56(5):732-753 (2014).
C. Zhang & Y. Liu
Multicategory Angle-based Large-margin Classification
Biometrika 101(3):625-640 (2014).
S. Bhamidi, G. Bresler & A. Sly
Mixing time of exponential random graphs
49th Annual IEEE Symposium on Foundations of Computer Science 803-812 (2008).
A. R. Gallant, C. Ji & B.S. Lee
A Gaussian Approximation Scheme for Computation of Option Prices in Stochastic Volatility Models
Journal of Econometrics, 146(1):44-58 (2008).
P. Hall, J. S. Marron & A. Neeman
Geometric Representation of High Dimension, Low Sample Size Data
Journal of the Royal Statistical Society: Series B 67(3):427-444 (2005).