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Activity Number: 557 - New Directions in Bayesian Methods for Longitudinal and Graph Data
Type: Contributed
Date/Time: Thursday, August 11, 2022 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #322359
Title: Methodological Improvement of Bayesian Additive Regression Trees for Classification Problems Using Novel Priors or Model Averaging
Author(s): Xiao Li* and Rodney A Sparapani and Purushottam W Laud and Brent R Logan
Companies: Medical College of Wisconsin and Medical College of Wisconsin and Medical College of Wisconsin and Medical College of Wisconsin
Keywords: Variance reduction; probit regression; binary/dichotomous outcomes; hematopoietic stem cell transplant

As an ensemble of decision trees method, Bayesian additive regression trees (BART) has the ability to detect and model non-linear relationships and complex interactions automatically. Its excellent predictive performance has been demonstrated in a wide range of data structures. However, BART predictions often have an inflated variation compared to other models, especially in noisy settings such as binary outcomes in classification problems. In this paper, we focus on binary outcomes and introduce two approaches to integrate BART with Bayesian probit linear regression, to stabilize the predictions and shrink the variance of the mean function. The first approach uses the linear model as a centering function, and applies an enhanced version of BART to the residuals which adapts to the degree of non-linearity in the function. The second approach uses Bayesian model averaging, with pseudo-marginal likelihood to determine the weights. The performance of both methods are evaluated and compared on simulated data. A real data study of safety outcomes for voluntary unrelated donors providing hematopoietic stem cells for a transplant demonstrates the proposed methods.

Authors who are presenting talks have a * after their name.

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