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stat.ME2021
Variable Selection Using Bayesian Additive Regression Trees
Chuji Luo, Michael J. Daniels
Variable selection is an important statistical problem. This problem becomes more challenging when the candidate predictors are of mixed type (e.g. continuous and binary) and impac…
stat.CO2021
BNPqte: A Bayesian Nonparametric Approach to Causal Inference on Quantiles in R
Chuji Luo, Michael J. Daniels
In this article, we introduce the BNPqte R package which implements the Bayesian nonparametric approach of Xu, Daniels and Winterstein (2018) for estimating quantile treatment effe…
stat.ME2021
Inference for BART with Multinomial Outcomes
Yizhen Xu, Joseph W. Hogan, Michael J. Daniels +2
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) m…