Dr. HE Jingyu 何靖宇博士 Department of Management Sciences
Assistant Professor

Address
7-278, Lau Ming Wai Academic Building, City University of Hong Kong

Phone
+852 34424753

Fax
+852 34420189

Email
jingyuhe@cityu.edu.hk

Personal Web
http://www.jingyuhe.com
Public CV
View

SSRN Web
http://papers.ssrn.com/author=2636615



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. November 2021; In: Journal of Econometrics.

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


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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