Jingyu He
Title
Cited by
Cited by
Year
Deep learning for predicting asset returns
G Feng, J He, NG Polson
arXiv preprint arXiv:1804.09314, 2018
572018
Regularization and confounding in linear regression for treatment effect estimation
PR Hahn, CM Carvalho, D Puelz, J He
Bayesian Analysis 13 (1), 163-182, 2018
562018
XBART: Accelerated bayesian additive regression trees
J He, S Yalov, PR Hahn
The 22nd International Conference on Artificial Intelligence and Statistics, 2019
37*2019
Efficient sampling for Gaussian linear regression with arbitrary priors
PR Hahn, J He, H Lopes
Journal of Computational and Graphical Statistics, 2018
36*2018
Bayesian factor model shrinkage for linear IV regression with many instruments
PR Hahn, J He, H Lopes
Journal of Business & Economic Statistics 36 (2), 278-287, 2018
182018
Factor investing: A bayesian hierarchical approach
G Feng, J He
Journal of Econometrics, 2021
11*2021
Stochastic tree ensembles for regularized nonlinear regression
J He, PR Hahn
Journal of the American Statistical Association, 1-61, 2021
9*2021
Bayesian Inference for Gamma Models
J He, N Polson, J Xu
arXiv preprint arXiv:2106.01906, 2021
42021
Asset Pricing with Panel Trees under Global Split Criteria
X He, LW Cong, G Feng, J He
Available at SSRN 3949463, 2021
12021
Stochastic Tree Ensembles for Estimating Heterogeneous Effects
N Krantsevich, J He, PR Hahn
URL: https://math. la. asu. edu/~ prhahn/XBCF. pdf, 2021
12021
XBART: A Scalable Stochastic Algorithm for Supervised Machine Learning with Additive Tree Ensembles
J He
PQDT-UK & Ireland, 2020
2020
Supplement to “XBART: Accelerated Bayesian Additive Regression Trees”
J He, S Yalov, PR Hahn
2019
bayeslm: Efficient Sampling for Gaussian Linear Regression with Arbitrary Priors
PR Hahn, J He, H Lopes
http://cran.ma.imperial.ac.uk/web/packages/bayeslm/bayeslm.pdf, 2018
2018
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Articles 1–13