1.6k citations · 1.6k across the 10 of their papers we have counts for
10 papers
Influential Observations in Bayesian Regression Tree Models
Matthew T. Pratola, Edward I. George, Robert E. McCulloch
BCART (Bayesian Classification and Regression Trees) and BART (Bayesian Additive Regression Trees) are popular Bayesian regression models widely applicable in modern regression pro…
Causal Inference with the Instrumental Variable Approach and Bayesian Nonparametric Machine Learning
Robert E. McCulloch, Rodney A. Sparapani, Brent R. Logan +1
We provide a new flexible framework for inference with the instrumental variable model. Rather than using linear specifications, functions characterizing the effects of instruments…
Fully Nonparametric Bayesian Additive Regression Trees
Edward George, Prakash Laud, Brent Logan +2
Bayesian Additive Regression Trees (BART) is a fully Bayesian approach to modeling with ensembles of trees. BART can uncover complex regression functions with high dimensional regr…
Nonparametric competing risks analysis using Bayesian Additive Regression Trees (BART)
Rodney Sparapani, Brent R. Logan, Robert E. McCulloch +1
Many time-to-event studies are complicated by the presence of competing risks. Such data are often analyzed using Cox models for the cause specific hazard function or Fine-Gray mod…
Decision making and uncertainty quantification for individualized treatments
Brent R. Logan, Rodney Sparapani, Robert E. McCulloch +1
Individualized treatment rules (ITR) can improve health outcomes by recognizing that patients may respond differently to treatment and assigning therapy with the most desirable pre…
Heteroscedastic BART Using Multiplicative Regression Trees
Matthew Pratola, Hugh Chipman, Edward George +1
BART (Bayesian Additive Regression Trees) has become increasingly popular as a flexible and scalable nonparametric regression approach for modern applied statistics problems. For t…
mBART: Multidimensional Monotone BART
Hugh A. Chipman, Edward I. George, Robert E. McCulloch +1
For the discovery of regression relationships between Y and a large set of p potential predictors x 1 , . . . , x p , the flexible nonparametric nature of BART (Bayesian Additive R…
Parallel Bayesian Additive Regression Trees
Matthew T. Pratola, Hugh A. Chipman, James R. Gattiker +3
Bayesian Additive Regression Trees (BART) is a Bayesian approach to flexible non-linear regression which has been shown to be competitive with the best modern predictive methods su…
Discussion of "Statistical Inference: The Big Picture" by R. E. Kass
Robert McCulloch
Discussion of "Statistical Inference: The Big Picture" by R. E. Kass [arXiv:1106.2895]
BART: Bayesian additive regression trees
Hugh A. Chipman, Edward I. George, Robert E. McCulloch
We develop a Bayesian "sum-of-trees" model where each tree is constrained by a regularization prior to be a weak learner, and fitting and inference are accomplished via an iterativ…