| Deep learning for predicting asset returns G Feng, J He, NG Polson arXiv preprint arXiv:1804.09314, 2018 | 94 | 2018 |
| 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 | 86 | 2018 |
| Deep Learning in Characteristics-Sorted Factor Models G Feng, J He, N Polson, J Xu Available on SSRN 3243683, 2022 | 60* | 2022 |
| XBART: Accelerated bayesian additive regression trees J He, S Yalov, PR Hahn The 22nd International Conference on Artificial Intelligence and Statistics, 2019 | 58* | 2019 |
| Efficient sampling for Gaussian linear regression with arbitrary priors PR Hahn, J He, H Lopes Journal of Computational and Graphical Statistics, 2018 | 45* | 2018 |
| Stochastic tree ensembles for regularized nonlinear regression J He, PR Hahn Journal of the American Statistical Association 118 (541), 551-570, 2023 | 23* | 2023 |
| 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 | 22 | 2018 |
| Factor investing: A Bayesian hierarchical approach G Feng, J He Journal of Econometrics 230 (1), 183-200, 2022 | 17* | 2022 |
| Asset pricing with panel tree under global split criteria LW Cong, G Feng, J He, X He National Bureau of Economic Research, 2023 | 15* | 2023 |
| Stochastic tree ensembles for estimating heterogeneous effects N Krantsevich, J He, PR Hahn International Conference on Artificial Intelligence and Statistics, 6120-6131, 2023 | 8 | 2023 |
| Data Augementation with Polya Inverse Gamma J He, NG Polson, J Xu arXiv preprint arXiv:1905.12141, 2019 | 7* | 2019 |
| Uncommon Factors for Bayesian Asset Clusters LW Cong, G Feng, J He, J Li Available at SSRN 4219905, 2022 | 5 | 2022 |
| Deep learning for predicting asset returns. arXiv G Feng, J He, NG Polson arXiv preprint arXiv:1804.09314, 2018 | 5 | 2018 |
| Local Gaussian process extrapolation for BART models with applications to causal inference M Wang, J He, PR Hahn https://arxiv.org/abs/2204.10963, 2022 | 2 | 2022 |
| Statement of Research J He | | 2022 |
| XBART: A Scalable Stochastic Algorithm for Supervised Machine Learning with Additive Tree Ensembles J He The University of Chicago, 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 |
| Slides: Uncommon Factors for Bayesian Asset Clusters J He http://jingyuhe.com/files/BCM.pdf, 0 | | |
P. Richard HahnArizona State UniversityVerified email at asu.edu
