Teaching
(Beginning 2022 Spring, we fully switch to Canvas and the following websites will no longer be updated.)
- STAT425: Introduction to Nonparametric Statistics
- STAT491: Introduction to Stochastic Processes
- STAT498: Introduction to Stochastic Processes II
- STAT513: Statistical Inference
- STAT519: Time Series Analysis
- STAT520: Spectral Analysis of Time Series
- STAT535: Foundations of Machine Learning
- STAT559: Measure Theory
- STAT581: Advanced Theory of Statistical Inference I
- STAT583: Advanced Theory of Statistical Inference III
Journal Publications
- Rank- and graph-based methods -
On Rosenbaum's rank-based matching estimator
Matias D. Cattaneo, Fang Han, and Zhexiao Lin
On the adaptation of causal forests to manifold data
Yiyi Huo, Yingying Fan, and Fang Han
On propensity score matching with a diverging number of matches
Yihui He and Fang Han
On regression-adjusted imputation estimators of the average treatment effect
Zhexiao Lin and Fang Han
Azadkia-Chatterjee's correlation coefficient adapts to manifold data
Fang Han and Zhihan Huang
Limit theorems of Chatterjee's rank correlation
Zhexiao Lin and Fang Han
On the failure of the bootstrap for Chatterjee's rank correlation
Zhexiao Lin and Fang Han
Biometrika (in press).
On Azadkia-Chatterjee's conditional dependence coefficient
Hongjian Shi, Mathias Drton, and Fang Han
Bernoulli, 30(2): 851--877, 2024.
Estimation based on nearest neighbor matching: from density ratio to average treatment effect
Zhexiao Lin, Peng Ding, and Fang Han
Econometrica, 91(6):2187-2217, 2023.
On boosting the power of Chatterjee's rank correlation (program code)
Zhexiao Lin and Fang Han
Biometrika, 110(2):283-299, 2023.
(Most Read Article in the Journal)
Robust functional principal component analysis via a functional pairwise spatial sign operator
Ken Wang, Sisheng Liu, Fang Han, and Chongzhi Di
Biometrics, 79(2): 1239-1253, 2023.
On the power of Chatterjee's rank correlation
Hongjian Shi, Mathias Drton, and Fang Han
Biometrika, 109(2):317-333, 2022.
(Most Read Article in the Journal)
On extensions of rank correlation coefficients to multivariate spaces
Fang Han
Bernoulli News, 28(2): 7-11, 2021.
(2021 Bernoulli Society New Researcher Award Lecture)
On rank estimators in increasing dimensions
Yanqin Fan, Fang Han, Wei Li, and Andrew Zhou
Journal of Econometrics, 214(2):379-412, 2020.
High dimensional consistent independence testing with maxima of rank correlations
Mathias Drton, Fang Han, and Hongjian Shi
The Annals of Statistics, 48(6):3206-3227, 2020.
ECA: High dimensional elliptical component analysis in non-Gaussian distributions (program code)
Fang Han and Han Liu
Journal of the American Statistical Association - Theory and Methods, 113(521):252-268, 2018.
(Winner of the 2013 ICSA/ISBS Student Paper Award)
On inference validity of weighted U-statistics under data heterogeneity
Fang Han and Tianchen Qian
Electronic Journal of Statistics, 12(2):2637-2708, 2018.
Distribution-free tests of independence in high dimensions
Fang Han, Shizhe Chen, and Han Liu
Biometrika, 104(4):813-828, 2017.
Robust inference of risks of large portfolios
Jianqing Fan, Fang Han, Han Liu, and Byron Vickers
Journal of Econometrics, 194(2):298-308, 2016.
Sparse median graphs estimation in a high dimensional semiparametric model
Fang Han, Xiaoyan Han, Han Liu, and Brian Caffo
The Annals of Applied Statistics, 10(3):1397-1426, 2016.
(Winner of the 2014 David P. Byar Young Investigator Travel Award Sponsored by ASA Biometrics Section)
High dimensional semiparametric scale-invariant principal component analysis
Fang Han and Han Liu
IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(10):2016-2032, 2014.
Scale-invariant sparse PCA on high dimensional meta-elliptical data
Fang Han and Han Liu
Journal of the American Statistical Association - Theory and Methods, 109(505):275-287, 2014.
CODA: high dimensional copula discriminant analysis
Fang Han, Tuo Zhao, and Han Liu
Journal of Machine Learning Research, 14:629-671, 2013.
High dimensional semiparametric Gaussian copula graphical models
Han Liu, Fang Han, Ming Yuan, John Lafferty, and Larry Wasserman
The Annals of Statistics, 40(4):2293-2326, 2012.
(Winner of the 2013 David P. Byar Young Investigator
Travel Award Sponsored by ASA Biometrics Section)
- Statistical optimal transport (OT) and OT-induced ranks -
Distribution-free tests of multivariate independence based on center-outward quadrant, Spearman, Kendall, and van der Waerden statistics
Hongjian Shi, Marc Hallin, Mathias Drton, and Fang Han
Bernoulli (in press).
(used to titled "Center-outward sign- and rank-based quadrant, Spearman, and Kendall tests for multivariate independence")
On universally consistent and fully distribution-free rank tests of vector independence
Hongjian Shi, Marc Hallin, Mathias Drton, and Fang Han
The Annals of Statistics, 50(4): 1933-1959, 2022.
(used to titled "Rate-optimality of Consistent Distribution-free Tests of Independence based on Center-outward Ranks and Signs")
Distribution-free consistent independence tests via center-outward ranks and signs (program code)
Hongjian Shi, Mathias Drton, and Fang Han
Journal of the American Statistical Association - Theory and Methods, 117(537): 395-410, 2022.
- Mixture models -
Fisher-Pitman permutation tests based on nonparametric Poisson mixtures with application to single cell genomics
Zhen Miao, Weihao Kong, Ramya Vinayak, Wei Sun, and Fang Han
Journal of the American Statistical Association - Theory and Methods (in press).
Nonparametric mixture MLEs under Gaussian-smoothed optimal transport distance
Fang Han, Zhen Miao, and Yandi Shen
IEEE Transactions on Information Theory, 69(12):7823-7835, 2023.
A composite likelihood approach to latent multivariate
Gaussian modeling of SNP data with application to genetic association testing
Fang Han and Wei Pan
Biometrics, 68(1):307-315, 2012.
- Nonparametric and semiparametric regressions -
Adaptive estimation of high dimensional partially linear model (program) (supplement)
Fang Han, Zhao Ren, and Yuxin Zhu
On a phase transition in general order spline regression
Yandi Shen, Qiyang Han, and Fang Han
IEEE Transactions on Information Theory, 67(8): 5283-5304, 2021.
Optimal estimation of variance in nonparametric regression with random design
Yandi Shen, Chao Gao, Daniela Witten, and Fang Han
The Annals of Statistics, 48(6):3589-3618, 2020.
On estimation of isotonic piecewise constant signals
Chao Gao, Fang Han, and Cun-Hui Zhang
The Annals of Statistics, 48(2):629-654, 2020.
A provable smoothing approach for high dimensional generalized regression with applications in genomics
Fang Han, Hongkai Ji, Zhicheng Ji, and Honglang Wang
Electronic Journal of Statistics, 11(2):4347-4403, 2017.
- Time series analysis -
Tail behavior of dependent V-statistics and its applications
Yandi Shen, Fang Han, and Daniela Witten
Estimation and inference on Granger causality in a latent high-dimensional Gaussian vector autoregressive model
Yanqin Fan, Fang Han, and Hyeonseok Park
Journal of Econometrics, 237(1): 105513, 2023.
Probability inequalities for high dimensional time series under a triangular array framework
Fang Han and Wei Biao Wu
in Handbook of Engineering Statistics, 2nd ed, Springer, 2023.
Moment bounds for large autocovariance matrices under dependence
Fang Han and Yicheng Li
Journal of Theoretical Probability, 33:1445-1492, 2020.
Exponential inequalities for dependent V-statistics via random Fourier features
Yandi Shen, Fang Han, and Daniela Witten
Electronic Journal of Probability, 25(7):1-18, 2020.
An exponential inequality for U-statistics under mixing conditions
Fang Han
Journal of Theoretical Probability, 31:556-578, 2018.
Joint estimation of multiple graphical models from high dimensional dependent data
Huitong Qiu, Fang Han, Han Liu, and Brian Caffo
Journal of Royal Statistical Society, Series B, 78(2):487-504, 2016.
(Winner of the 2014 ENAR Distinguished Student Paper Award)
A direct estimation of high dimensional stationary vector autoregressions
Fang Han, Huanran Lu, and Han Liu
Journal of Machine Learning Research, 16:3115-3150, 2015.
- Random matrix theory -
Robust scatter matrix estimation for high dimensional distributions with heavy tail
Junwei Lu, Fang Han, and Han Liu
IEEE Transactions on Information Theory, 67(8):5283-5304, 2021.
Asymptotic joint distribution of extreme eigenvalues and trace of large sample covariance matrix in a generalized spiked population model
Zeng Li, Fang Han, and Jianfeng Yao
The Annals of Statistics, 48(6):3138-3160, 2020.
An extreme-value approach for testing the equality of large U-statistic based correlation matrices
Cheng Zhou, Fang Han, Xin-Sheng Zhang, and Han Liu
Bernoulli, 25(2):1472-1503, 2019.
On Gaussian comparison inequality and its application to spectral analysis of large random matrices
Fang Han, Sheng Xu, and Wen-Xin Zhou
Bernoulli, 24(3):1787-1833, 2018.
Statistical analysis of latent generalized correlation matrix estimation in transelliptical distribution
Fang Han and Han Liu
Bernoulli, 23(1):23-57, 2017.
- Others -
Challenges of big data analysis
Jianqing Fan, Fang Han, and Han Liu
National Science Review, 1(3):293-314, 2014.
(Most Read Article in the Journal, NSR 2015 Best Paper)
Searching for differentially expressed genes by PLS-VIP method
Fang Han, Jingchen Wu, Jiangfeng Xu, and Minghua Deng
Acta Scientiarum Naturalium Universitatis Pekinensis, 45(1):1-5, 2010.
- Applications -
The Baltimore declaration toward the exploration of organoid intelligence
with Thomas Hartung, Donald J. Zack, and et al.
Frontiers in Science, 1, 2023.
First organoid intelligence (OI) workshop to form an OI community
with Thomas Hartung, Joshua T Vogelstein, and et al.
Frontiers in Artificial Intelligence, 6, 2023.
Shell microelectrode arrays (MEAs) for brain organoids
with Qi Huang, David Gracias, and et al.
Science Advances, 8:33, 2022.
(Featured on the Front of the Journal Website)
Individual level differential expression analysis for single cell RNA-seq data
with Mengqi Zhang, Wei Sun, and et al.
Genome Biology, 23:33, 2022.
Genome-wide
profiling of multiple histone methylations in olfactory cells: further implications for
cellular susceptibility to oxidative stress in schizophrenia
with Shinichi Kano, Akira Sawa, and et al.
Nature: Molecular Psychiatry, 18(7):740-742, 2013.
Automated diagnoses of attention defficit hyperactive disorder using MRI
with Ani Eloyan, Brian Caffo, and et al.
Frontiers in Systems Neuroscience, 6:61, 2012.
(Winner of the ADHD-200 Global Competition for
Achieving the Highest Prediction Performance of Imaging-Based Diagnostic Classification
Algorithm)
Powerful multi-marker association tests: unifying genomic distance-based regression and logistic regression
Fang Han and Wei Pan
Genetic Epidemiology, 34(7):680-688, 2010.
A data-adaptive sum test for disease association with multiple common or rare variants
Fang Han and Wei Pan
Human Heredity, 70:42-54, 2010.
Test selection with application to detecting disease association with multiple SNPs
Wei Pan, Fang Han, and Xiaotong Shen
Human Heredity, 69:120-130, 2010.
Peer-reviewed conference publications
Robust portfolio optimization
Huitong Qiu, Fang Han, Han Liu, and Brian Caffo
Neural Information Processing Systems (NIPS), 28, 2015.
(Winner of the 2014 Student/Young Researcher Paper Award Sponsored by ASA
Risk Analysis Section)
Robust estimation of transition matrices in high dimensional heavy-tailed vector autoregressive processes
Huitong Qiu, Sheng Xu, Fang Han, Han Liu, and Brian Caffo
International Conference on Machine Learning (ICML), 32, 2015.
Context aware group nearest shrunken centroids in large-scale genomic studies
Juemin Yang, Fang Han, Rafael Irizarry, and Han Liu
Journal of Machine Learning Research (AISTATS track), 17, 2014.
Robust sparse principal component regression under the high dimensional elliptical model
Fang Han and Han Liu
Neural Information Processing Systems (NIPS), 26, 2013.
(Spotlight Presentation)
Transition matrix estimation in high dimensional vector autoregressive models
Fang Han and Han Liu
International Conference on Machine Learning (ICML), 30, 2013.
Sparse principal component analysis for high dimensional multivariate time series
Zhaoran Wang, Fang Han, and Han Liu
Journal of Machine Learning Research (AISTATS track), 16, 2013.
(Winner of the 2013 AISTATS Notable Paper Award)
Principal component analysis on non-Gaussian dependent data
Fang Han and Han Liu
International Conference on Machine Learning (ICML), 30, 2013.
(Winner of the 2013 ENAR Distinguished Student Paper Award)
Transelliptical component analysis
Fang Han and Han Liu
Neural Information Processing Systems (NIPS), 25, 2012.
(Oral Presentation). R package SMART available online
Semiparametric principal component analysis
Fang Han and Han Liu
Neural Information Processing Systems (NIPS), 25, 2012.
Transelliptical graphical models
Han Liu, Fang Han, and Cun-hui Zhang
Neural Information Processing Systems (NIPS), 25, 2012.
The nonparanormal SKEPTIC
Han Liu, Fang Han, Ming Yuan, John Lafferty, and Larry Wasserman
International Conference on Machine Learning (ICML), 29, 2012.
Unpublished technical reports
Kolmogorov dependence theory
Huitong Qiu, Fang Han, Han Liu, and Brian Caffo
Transelliptical graphical modeling under a hierarchical latent variable framework
Han Liu, Fang Han, and Cun-hui Zhang
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