Associate Director, Institute of Digital Economy and Innovation
Academic & Professional Qualification
PhD in Statistics, The Wharton School of Business, University of Pennsylvania
MS in Statistics, The Wharton School of Business, University of Pennsylvania
BS in Statistics, School of Mathematical Sciences, Peking University
BS in Economics, Center for Economic Research, Peking University
Dan Yang received her Ph.D. degree in Statistics from the Wharton School of Business, University of Pennsylvania in 2012. She is an assistant professor in the Department of Statistics and Biostatistics at Rutgers University from 2013.
MSBA7011 Managing and Mining Big Data
MSBA7013 Forecasting and Predictive Analytics
High-dimensional statistical inference
Xin Chen, Dan Yang, Yan Xu, Yin Xia, Dong Wang, and Haipeng Shen (Forthcoming). Testing and Support Recovery of Correlation Structures for Matrix-Valued Observations with an Application to Stock Market Data. Journal of Econometrics.
Rong Chen, Dan Yang, and Cun-Hui Zhang (2022). Factor Models for High-Dimensional Tensor Time Series. Journal of the American Statistical Association, 117(537):94-116.
Rong Chen, Han Xiao, and Dan Yang (2021). Autoregressive Models for Matrix-valued Time Series. Journal of Econometrics, 222(1):539-560.
Dan Yang, Zongming Ma, and Andreas Buja (2016). Rate Optimal Denoising of Simultaneously Sparse and Low Rank Matrices. Journal of Machine Learning Research, 17:1-27.
Gen Li, Dan Yang, Andrew B. Nobel, and Haipeng Shen (2016). Supervised Singular Value Decomposition and Its Asymptotic Properties. Journal of Multivariate Analysis, 146:7-17.
Dan Yang, Zongming Ma, and Andreas Buja (2014). A Sparse Singular Value Decomposition Method for High-Dimensional Data. Journal of Computational and Graphical Statistics, 23(4):923-942.
Dan Yang and Dylan S. Small (2013). An R Package and a Study of Methods for Computing Empirical Likelihood. Journal of Statistical Computation and Simulation, 83(7):1363-1372.
Dan Yang, Dylan S. Small, Jeffrey H. Silber, and Paul R. Rosenbaum (2012). Optimal Matching with Minimal Deviation from Fine Balance in a Study of Obesity and Surgical Outcomes. Biometrics, 68(2):628-636.
NSF BIGDATA, Statistical Learning with Large Dynamic Tensor Data, 2017-2020