Fang Han
Department of Statistics
University of Washington

B-308, Padelford Hall
University of Washington
Seattle, WA 98195
fanghan AT UW DOT edu

I am an assistant professor in statistics and an adjunct assistant professor in economics.

I am interested in anything that can be formulated using U-statistics in a beautiful way.

Several areas I have been working in:
(i) high dimensional nonlinear statistics and processes;
(ii) distribution-free inference;
(iii) nonparametric and semiparametric statistics;
(iv) structural time series analysis;
(v) large random matrix analysis.

My research is supported by NSF DMS-1712536. Thanks, NSF!


STAT491

STAT498

STAT535

STAT559

STAT583


Publications:

My Google Scholar Profile

Preprints:

Technical Reports on arXiv

Distribution-free Consistent Independence Tests via Hallin's Multivariate Rank
Hongjian Shi, Mathias Drton, and Fang Han

Probability Inequalities for High Dimensional Time Series under a Triangular Array Framework
Fang Han and Wei Biao Wu

Optimal Estimation of Variance in Nonparametric Regression with Random Design
Yandi Shen, Chao Gao, Daniela Witten, and Fang Han

Tail Behavior of Dependent V-statistics and its Applications
Yandi Shen, Fang Han, and Daniela Witten

Adaptive Estimation of High Dimensional Partially Linear Model (program) (supplement)
Fang Han, Zhao Ren, and Yuxin Zhu

Peer-Reviewed Journal Publications:

High Dimensional Independence Testing with Maxima of Rank Correlations
Mathias Drton, Fang Han, and Hongjian Shi
The Annals of Statistics (in press).

On Rank Estimators in Increasing Dimensions
Yanqin Fan, Fang Han, Wei Li, and Andrew Zhou
Journal of Econometrics (in press).

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 (in press).

Moment Bounds for Large Autocovariance Matrices under Dependence
Fang Han and Yicheng Li
Journal of Theoretical Probability (in press).

On Estimation of Isotonic Piecewise Constant Signals
Chao Gao, Fang Han, and Cun-Hui Zhang
The Annals of Statistics (in press).

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 Inference Validity of Weighted U-statistics under Data Heterogeneity
Fang Han and Tianchen Qian
Electronic Journal of Statistics, 12(2):2637--2708, 2018.

ECA: High Dimensional Elliptical Component Analysis in non-Gaussian Distributions
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)

An Exponential Inequality for U-Statistics under Mixing Conditions
Fang Han
Journal of Theoretical Probability, 31(1):556--578, 2018.

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.

Distribution-Free Tests of Independence in High Dimensions
Fang Han, Shizhe Chen, and Han Liu
Biometrika, 104(4):813-828, 2017.

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.

Statistical Analysis of Latent Generalized Correlation Matrix Estimation in Transelliptical Distribution
Fang Han and Han Liu
Bernoulli, 23(1):23--57, 2017.

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)

Robust Inference of Risks of Large Portfolios
Jianqing Fan, Fang Han, Han Liu, and Byron Vickers
Journal of Econometrics, 194(2):298--308, 2016.

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.

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.

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)

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)

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, 2011.

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.

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.

Peer-Reviewed Journal Publications (Collaborative Work):

Genome-Wide Profiling of Multiple Histone Methylations in Olfactory Cells: Further Implications for Cellular Susceptibility to Oxidative Stress in Schizophrenia
with Shinichi Kano et al.
Nature: Molecular Psychiatry, 18(7):740--742, 2013.

Automated Diagnoses of Attention Defficit Hyperactive Disorder using MRI
with Ani Eloyan 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)

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.