Hierarchical Embedded Bayesian Additive Regression Trees

• URL: http://arxiv.org/abs/2204.07207v2
• Date: Mon, 24 Apr 2023 14:08:48 GMT
• Title: Hierarchical Embedded Bayesian Additive Regression Trees
• Authors: Bruna Wundervald, Andrew Parnell, Katarina Domijan
• Abstract summary: HE-BART allows for random effects to be included at the terminal node level of a set of regression trees. Using simulated and real-world examples, we demonstrate that HE-BART yields superior predictions for many of the standard mixed effects models' example data sets. In a future version of this paper, we outline its use in larger, more advanced data sets and structures.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a simple yet powerful extension of Bayesian Additive Regression Trees which we name Hierarchical Embedded BART (HE-BART). The model allows for random effects to be included at the terminal node level of a set of regression trees, making HE-BART a non-parametric alternative to mixed effects models which avoids the need for the user to specify the structure of the random effects in the model, whilst maintaining the prediction and uncertainty calibration properties of standard BART. Using simulated and real-world examples, we demonstrate that this new extension yields superior predictions for many of the standard mixed effects models' example data sets, and yet still provides consistent estimates of the random effect variances. In a future version of this paper, we outline its use in larger, more advanced data sets and structures.

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