I received a BS in Mathematical Statistics from Nankai University in May 2006, and a PhD in Statistical Science from Duke University in May 2010. My research interests include graphical/factor models, Bayesian time series and statistical disclosure limitation. Outside of school, I spend my time on tennis, pingpong, poker, backpacking, and reading about financial markets.
Hao Wang, 2012, Two New Algorithms for Solving Covariance Graphical Lasso Based on Coordinate Descent and ECM. [html]
Minhua Chen, Hao Wang, Xuejun Liao and Lawrence Carin, Bayesian Learning of Sparse Gaussian Graphical Models. [pdf]
H. Wang and Natesh S. Pillai, 2011, On a Class of Shrinkage Priors for Covariance Matrix Estimation. [html]
H. Wang, 2011, The Bayesian Graphical Lasso and Efficient Posterior Computation. [html]
H. Wang, 2010, Bayesian Analysis of Gaussian Graphical Models for Correlated Sample. [html]
H. Wang and Sophia Zhengzi Li, 2012, Efficient Gaussian Graphical Model Determination under G-Wishart Prior Distributions. Electronic Journal of Statistics 6 (2012):168-198 [html]
H. Wang and Jerry Reiter, Multiple Imputation for Sharing Precise Geographies in Public Use Data. Annals of Applied Statistics (to appear) [html]
H. Wang, C. Reeson and C. M. Carvalho, Dynamic Financial Index Models: Modeling Conditional Dependencies via Graphs. Bayesian Analysis 6 (2011): 639-664[html]
H. Wang and C. M. Carvalho, Simulation of Hyper-Inverse Wishart Distributions for Non-decomposable Graphs. Electronic Journal of Statistics 4 (2010):1467-1470 [html]
H. Wang, Sparse Seemingly Unrelated Regression Modelling: Applications in Econometrics and Finance. Computational Statistics and Data Analysis 54 (2010): 2866-2877[html]
H. Wang and M. West, Bayesian analysis of matrix normal graphical models. Biometrika 96 (2009):821-834 [html]
Stat 110 Introduction to Statistical Reasoning (Spring 2012).
Stat 509 Statistics for Engineers (Fall 2011).
Stat 720 Time Series Analysis (Spring 2011).
Stat 509 Statistics for Engineers (Fall 2010).