I am an associate professor of Statistics at ASU. I develop probability models and computational techniques for applied data analysis, with a focus on the behavioral, social, and health sciences. My specific research interests include regression tree methods, causal inference from observational data, and foundations of statistics.
Amir Bashir, Carlos M. Carvalho, PRH, and M. Beatrix Jones. Post-Processing Posteriors Over Precision
Matrices to Produce Sparse Graph Estimates
Jingyu He, Saar Yalov, and PRH Accelerated Bayesian additive regression trees
PRH, Jared S. Murray, and Carlos M. Carvalho. Bayesian regression tree models for causal inference: regularization, confounding, and heterogeneous effects
bayeslm: R package for fitting Bayesian regularized linear regression models.
bcf: Bayesian Causal Forests for binary treatment and continuous response.
2017 Atlantic Causal Inference Conference Data Analysis Challenge
Results and R scripts
- Jingyu He, Saar Yalov, and PRH Accelerated Bayesian additive regression trees
- Amir Bashir, Carlos M. Carvalho, PRH, and M. Beatrix Jones. Post-Processing Posteriors Over Precision
Matrices to Produce Sparse Graph Estimates. Accepted at Bayesian Analysis.
- PRH, Jingyu He and Hedibert Lopes. Efficient sampling for Gaussian linear regression with arbitrary priors. ( Demo script.)
- PRH, Ryan Martin and Stephen G. Walker. On recursive predictive distributions. Journal of the American Statistical Association, forthcoming.
- David Puelz, PRH, and Carlos M. Carvalho. Variable selection in seemingly unrelated regressions with random predictors. Bayesian analysis, forthcoming.
- PRH, Carlos M. Carvalho, Jingyu He and David Puelz. Regularization and confounding in linear regression for treatment effect estimation. Bayesian analysis, forthcoming.
- PRH, Jingyu He, and Hedibert Lopes (2016). Bayesian factor model shrinkage for linear IV regression with many instruments. Journal of Business and Economic Statistics, forthcoming.
- PRH, J. S. Murray, and I. Manolopoulou (2016). A Bayesian partial identification approach to inferring the prevalence of accounting misconduct
. Journal of the American Statistical Association, 111 (513), 14-26.
- PRH, Carl F. Mela, and Indranil Goswami (2015). A Bayesian hierarchical model for inferring player strategy types in a number guessing game. Annals of Applied Statistics 2015, Vol. 9, No. 3, 1459-1483. (supplement, data, read-me file, code)
- PRH and Carlos M. Carvalho (2015). Decoupling shrinkage and selection in Bayesian linear models: a posterior summary perspective. Journal of the American Statistical Association 110 (509), 435-448.
- PRH, Sayan Mukherjee, and Carlos M. Carvalho (2013). Partial Factor Modeling: Predictor Dependent Shrinkage for Linear Regression. Journal of the American Statistical Association 108 (503), 999-1008.
- PRH, James Scott, and Carlos M. Carvalho (2012). A sparse factor-analytic probit model for congressional voting patterns. Journal of the Royal Statistical Society: Series C 61 (4), 619-635.
- PRH, Jared S. Murray, and Carlos M. Carvalho. Bayesian regression tree models for causal inference: regularization, confounding, and heterogeneous effects.
- Michelle Xia and PRH. A finite mixture model approach to regression under covariate misclassification.
- PRH. Predictivist Bayes density estimation.
Working papers and research notes
- David Puelz, PRH, Carlos M. Carvalho. Regret-based selection for sparse dynamic portfolios
- Joseph Gerakos, PRH, Andrei Kovrijnykh, and Frank Zhou. Prediction versus Inducement and the Informational Efficiency of Going Concern Opinions
- PRH. An illustration of the risk of borrowing information via a shared likelihood.
- PRH and Lane F. Burgette. An approximate likelihood for simultaneous nonlinear quantile regression
- Lane F. Burgette and PRH. A symmetric prior for multinomial probit models