5 citations · 7 across the 2 of their papers we have counts for
6 papers
Stochastic Tree Ensembles for Estimating Heterogeneous Effects
Nikolay Krantsevich, Jingyu He, P. Richard Hahn
Determining subgroups that respond especially well (or poorly) to specific interventions (medical or policy) requires new supervised learning methods tailored specifically for caus…
Bayesian Inference for Gamma Models
Jingyu He, Nicholas Polson, Jianeng Xu
We use the theory of normal variance-mean mixtures to derive a data augmentation scheme for models that include gamma functions. Our methodology applies to many situations in stati…
Factor Investing: A Bayesian Hierarchical Approach
Guanhao Feng, Jingyu He
This paper investigates asset allocation problems when returns are predictable. We introduce a market-timing Bayesian hierarchical (BH) approach that adopts heterogeneous time-vary…
XBART: Accelerated Bayesian Additive Regression Trees
Jingyu He, Saar Yalov, P. Richard Hahn
Bayesian additive regression trees (BART) (Chipman et. al., 2010) is a powerful predictive model that often outperforms alternative models at out-of-sample prediction. BART is espe…
Efficient sampling for Gaussian linear regression with arbitrary priors
P. Richard Hahn, Jingyu He, Hedibert Lopes
This paper develops a slice sampler for Bayesian linear regression models with arbitrary priors. The new sampler has two advantages over current approaches. One, it is faster than…
Deep Learning for Predicting Asset Returns
Guanhao Feng, Jingyu He, Nicholas G. Polson
Deep learning searches for nonlinear factors for predicting asset returns. Predictability is achieved via multiple layers of composite factors as opposed to additive ones. Viewed i…