Dr. HE Jingyu
何靖宇博士
Assistant Professor
PhD - Econometrics and Statistics (The University of Chicago Booth School of Business)
MBA - Business Administration (The University of Chicago Booth School of Business)
MS - Statistics (The University of Chicago)
BS - Statistics (University of Science and Technology of China)

Biography

Dr. He is an assistant professor of business statistics at the City University of Hong Kong. He received Ph.D., M.B.A. and M.S. from The University of Chicago, and B.S. from University of Science and Technology of China. His research interests include Bayesian statistics, machine learning algorithm and quantitative finance.

Publications

He, Jingyu; Hahn, P. Richard / Stochastic tree ensembles for regularized nonlinear regression. March 2023; In: Journal of the American Statistical Association. Vol. 118, No. 541, pp. 551–570
Feng, Guanhao; He, Jingyu / Factor investing: A Bayesian hierarchical approach. September 2022; In: Journal of Econometrics. Vol. 230, No. 1, pp. 183-200
Hahn, P. Richard; He, Jingyu; Lopes, Hedibert F. / Efficient Sampling for Gaussian Linear Regression With Arbitrary Priors. 2019; In: Journal of Computational and Graphical Statistics. Vol. 28, No. 1, pp. 142-154
Hahn, P. Richard; He, Jingyu; Lopes, Hedibert / Bayesian Factor Model Shrinkage for Linear IV Regression With Many Instruments. April 2018; In: Journal of Business and Economic Statistics. Vol. 36, No. 2, pp. 278-287
Hahn, P. Richard; Carvalho, Carlos M.; Puelz, David; He, Jingyu / Regularization and Confounding in Linear Regression for Treatment Effect Estimation. 2018; In: Bayesian Analysis. Vol. 13, No. 1, pp. 163-182
Cong, Lin William; Feng, Guanhao; He, Jingyu; Li, Junye / Uncommon Factors for Bayesian Asset Clusters. September 2022;
Feng, Guanhao; He, Jingyu; Polson, Nick; Xu, Jianeng / Deep Learning in Characteristics-Sorted Factor Models. 2022;
Cong, Lin William; Feng, Guanhao; He, Jingyu; He, Xin / Asset Pricing with Panel Tree Under Global Split Criteria. October 2021;
He, Jingyu; Yalov, Saar; Hahn, P. Richard / XBART: Accelerated Bayesian Additive Regression Trees. April 2019; The 22nd International Conference on Artificial Intelligence and Statistics. pp. 1130-1138