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Dr. Dan YANG

Dr. Dan YANG

Innovation and Information Management

Associate Professor

3917 0015
dyanghku@hku.hk
KK 816
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
Biography

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.

Teaching
  • MSBA7011 Managing and Mining Big Data
  • MSBA7013 Forecasting and Predictive Analytics
Research Interest
  • High-dimensional statistical inference
  • Dimension reduction
  • Tensor data
  • Big data
Selected Publications
  • Gen Li, Dan Yang, Haipeng Shen, and Andrew B Nobel (2016). Supervised Singular Value Decomposition and Its Asymptotic Properties. Journal of Multivariate Analysis, 146:7-17.
  • 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.
  • 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.
  • Andreas Buja, Zongming Ma, and Dan Yang (2013). Optimal denoising of simultaneously sparse and low rank matrices in high dimensions. Communication, Control, and Computing (Allerton), 2013 51st Annual Allerton Conference on. IEEE, 445-447.
  • Dan Yang, Dylan 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, Volume 68, Issue 2, pages 628-636.
  • Dan Yang and Dylan Small (2012). An R package and a study of methods for computing empirical likelihood, Journal of Statistical Computation and Simulation, 83(7), 1363-1372.
  • Dan Yang, Dong Wang, Haipeng Shen and Hongtu Zhu (2018). Optimal functional bilinear regression with matrix covariates via reproducing kernel Hilbert space.
  • Rong Chen, Dan Yang, and Cunhui Zhang (2018). Factor models for tensor time series.
  • Rong Chen, Han Xiao, and Dan Yang (2018). Matrix autoregressive models.
  • Dong Wang, Dan Yang, Haipeng Shen and Hongtu Zhu (2018). On scalar-on-matrix bilinear regression analysis.
Grant

NSF BIGDATA, Statistical Learning with Large Dynamic Tensor Data, 2017-2020