1.6k citations · 1.6k across the 10 of their papers we have counts for
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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…
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…
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…