Address
7-278, Lau Ming Wai Academic Building, City University of Hong Kong
Phone
+852 34424753
Fax
+852 34420189
Email
Personal Web
http://www.jingyuhe.com
Public CV
Qualifications
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
Journal Publications and Reviews
Feng, Guanhao; He, Jingyu / Factor investing: A Bayesian hierarchical approach. September 2022; In: Journal of Econometrics. Vol. 230, No. 1, pp. 183-200
He, Jingyu; Hahn, P. Richard / Stochastic tree ensembles for regularized nonlinear regression. June 2021; In: Journal of the American Statistical Association.
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
Working Papers
Feng, Guanhao; He, Jingyu / Factor Investing: A Bayesian Hierarchical Approach. February 2019;
Chapters, Conference Papers, Creative and Literary Works
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
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