Inference for BART with Multinomial Outcomes

01/18/2021
by   Yizhen Xu, et al.
0

The multinomial probit Bayesian additive regression trees (MPBART) framework was proposed by Kindo et al. (KD), approximating the latent utilities in the multinomial probit (MNP) model with BART (Chipman et al. 2010). Compared to multinomial logistic models, MNP does not assume independent alternatives and the correlation structure among alternatives can be specified through multivariate Gaussian distributed latent utilities. We introduce two new algorithms for fitting the MPBART and show that the theoretical mixing rates of our proposals are equal or superior to the existing algorithm in KD. Through simulations, we explore the robustness of the methods to the choice of reference level, imbalance in outcome frequencies, and the specifications of prior hyperparameters for the utility error term. The work is motivated by the application of generating posterior predictive distributions for mortality and engagement in care among HIV-positive patients based on electronic health records (EHRs) from the Academic Model Providing Access to Healthcare (AMPATH) in Kenya. In both the application and simulations, we observe better performance using our proposals as compared to KD in terms of MCMC convergence rate and posterior predictive accuracy.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
09/30/2013

MPBART - Multinomial Probit Bayesian Additive Regression Trees

This article proposes Multinomial Probit Bayesian Additive Regression Tr...
research
02/16/2015

Particle Gibbs for Bayesian Additive Regression Trees

Additive regression trees are flexible non-parametric models and popular...
research
11/14/2018

Learning Optimal Personalized Treatment Rules Using Robust Regression Informed K-NN

We develop a prediction-based prescriptive model for learning optimal pe...
research
10/13/2019

Nonstationary Multivariate Gaussian Processes for Electronic Health Records

We propose multivariate nonstationary Gaussian processes for jointly mod...
research
01/26/2019

Distributed Learning with Compressed Gradient Differences

Training very large machine learning models requires a distributed compu...
research
10/28/2019

Multilevel Dimension-Independent Likelihood-Informed MCMC for Large-Scale Inverse Problems

We present a non-trivial integration of dimension-independent likelihood...

Please sign up or login with your details

Forgot password? Click here to reset