P. R. Hahn

发表

Decoupling Shrinkage and Selection in Bayesian Linear Models: A Posterior Summary Perspective

C. Carvalho, P. R. Hahn, By P. Richard Hahn, 2014, 1408.0464.

Sparse Factor-Analytic Probit Models

C. Carvalho, P. R. Hahn, 2008 .

Portfolio Selection for Individual Passive Investing

C. Carvalho, P. R. Hahn, David Puelz, 2019, Applied Stochastic Models in Business and Industry.

Symmetric Bayesian Multinomial Probit Models

Lane F Burgette, P. R. Hahn, 2010 .

Atlantic Causal Inference Conference (ACIC) Data Analysis Challenge 2017

Jared S. Murray, P. R. Hahn, Jared Murray, 2019, 1905.09515.

Partial Factor Modeling: Predictor-Dependent Shrinkage for Linear Regression

S. Mukherjee, C. Carvalho, P. R. Hahn, 2013 .

A national experiment reveals where a growth mindset improves achievement

Chris S. Hulleman, Angela L. Duckworth, C. Carvalho, 2019, Nature.

Predictor-dependent shrinkage for linear regression via partial factor modeling

S. Mukherjee, C. Carvalho, P. R. Hahn, 2010, 1011.3725.

A Symmetric Prior for Multinomial Probit Models

Lane F Burgette, P. R. Hahn, David Puelz, 2019, 1912.10334.

Optimal ETF Selection for Passive Investing

C. Carvalho, P. R. Hahn, David Puelz, 2015, 1510.03385.

Semi-supervised learning and the question of true versus estimated propensity scores

P. R. Hahn, Andrew Herren, 2020, 2009.06183.

Local Gaussian process extrapolation for BART models with applications to causal inference

P. R. Hahn, Jingyu He, Meijiang Wang, 2022, 2204.10963.

On recursive predictive distributions

S. Walker, Ryan Martin, P. R. Hahn, 2015 .

A finite mixture model approach to regression under covariate misclassification

P. R. Hahn, Michelle Xia, 2016, 1611.09408.

EXPLOITING SYMMETRY TO IMPROVE THE PERFORMANCE OF BRANCH-AND-BOUND FOR FINDING MAXIMUM INDEPENDENT SETS

P. R. Hahn, Bixiang Wang, O. Makhnin, 2007 .

Bayesian partial factor regression

P. R. Hahn, Carlos Carvalho McCombs, 2010 .

Statistical Aspects of SHAP: Functional ANOVA for Model Interpretation

P. R. Hahn, Andrew Herren, 2022, 2208.09970.

Feature selection in stratification estimators of causal effects: lessons from potential outcomes, causal diagrams, and structural equations

P. R. Hahn, Andrew Herren, 2022, 2209.11400.

Flexible prior specification for partially identified nonlinear regression with binary responses

Jared S. Murray, P. R. Hahn, Jared Murray, 2014 .

Post-Processing Posteriors Over Precision Matrices to Produce Sparse Graph Estimates

P. Richard Hahn, Carlos M. Carvalho, M. B. Jones, 2019 .

Statistical Aspects of SHAP: Functional ANOVA for Model Interpretation

P. R. Hahn, Andrew Herren, 2022, 2208.09970.

Bayesian decision theory for tree-based adaptive screening tests with an application to youth delinquency

P. Richard Hahn, Yi Zheng, Chelsea Krantsevich, 2021, CrimRxiv.

Elliptical slice sampling for Bayesian shrinkage regression with applications to causal inference

Jingyu He, P. Richard Hahn, P. R. Hahn, 2016 .

Shrinkage priors for linear instrumental variable models with many instruments

P. Richard Hahn, H. Lopes, P. R. Hahn, 2014, 1408.0462.

A Bayesian Partial Identification Approach to Inferring the Prevalence of Accounting Misconduct

P. Richard Hahn, Ioanna Manolopoulou, Jared S. Murray, 2014, 1407.8430.

Accelerated Bayesian Additive Regression Trees

Jingyu He, P. Richard Hahn, Saar Yalov, 2018, AISTATS.

Model specification via sequential coherence and backward induction

P. R. Hahn, 2015, 1502.06045.

Stochastic Tree Ensembles for Regularized Nonlinear Regression pdf

Jingyu He, P. Richard Hahn, P. R. Hahn, 2020, Journal of the American Statistical Association.

Fe b 20 19 XBART : Accelerated Bayesian Additive Regression Trees

P. R. Hahn, Jingyu He, Saar Yalov, 2019 .

1 4 M ar 2 01 9 XBART : Accelerated Bayesian Additive Regression Trees

P. R. Hahn, Jingyu He, Saar Yalov, 2019 .

Efficient Sampling for Gaussian Linear Regression With Arbitrary Priors

Jingyu He, P. Richard Hahn, Hedibert F. Lopes, 2018, Journal of Computational and Graphical Statistics.

A Survey of Learning Causality with Data

Ruocheng Guo, Huan Liu, P. Richard Hahn, 2018, ACM Comput. Surv..

Predictivist Bayes density estimation

P. R. Hahn, 2016 .

Probability Models for Targeted Borrowing of Information

P. Richard Hahn, P. R. Hahn, Richard Hahn, 2011 .

Sparse Factor-Analytic Probit Models

C. Carvalho, P. R. Hahn, James G. Scott, 2008 .

Stochastic Tree Ensembles for Estimating Heterogeneous Effects

Nikolay M. Krantsevich, P. R. Hahn, Jingyu He, 2022, AISTATS.

Estimating heterogeneous effects of continuous exposures using Bayesian tree ensembles: revisiting the impact of abortion rates on crime

Jared S. Murray, P. Richard Hahn, Carlos M. Carvalho, 2020, 2007.09845.

On Recursive Bayesian Predictive Distributions

Stephen G. Walker, P. Richard Hahn, Ryan Martin, 2015, Journal of the American Statistical Association.

Local Gaussian Process Extrapolation for BART Models with Applications to Causal Inference

P. R. Hahn, Jingyu He, Meijiang Wang, 2022, J. Comput. Graph. Stat..

Regret-based Selection for Sparse Dynamic Portfolios

David Puelz, P. Richard Hahn, Carlos M. Carvalho, 2017, 1706.10180.

A Survey of Learning Causality with Data: Problems and Methods

P. R. Hahn, Jundong Li, Ruocheng Guo, 2018, ArXiv.

The mesa distribution : an approximation likelihood for simultaneous nonlinear quantile regression

P. Richard Hahn, P. R. Hahn, Booth, 2012 .

A Survey of Learning Causality with Data

P. R. Hahn, Huan Liu, Jundong Li, 2018, ACM Comput. Surv..