Bayesian additive regression trees for multivariate skewed responses
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DOI: 10.1002/sim.9613
Abstract: This paper introduces a nonparametric regression approach for univariate and multivariate skewed responses using Bayesian additive regression trees (BART). Existing BART methods use ensembles of decision trees to model a mean function, and have become… read more here.
Keywords: multivariate skewed; additive regression; regression; methodology ... See more keywords
Bayesian Additive Regression Trees using Bayesian model averaging
Sign Up to like & getrecommendations! Published in 2018 at "Statistics and Computing"
DOI: 10.1007/s11222-017-9767-1
Abstract: Bayesian Additive Regression Trees (BART) is a statistical sum of trees model. It can be considered a Bayesian version of machine learning tree ensemble methods where the individual trees are the base learners. However, for… read more here.
Keywords: regression trees; model; trees using; bart bma ... See more keywords
BARP: Improving Mister P Using Bayesian Additive Regression Trees
Sign Up to like & getrecommendations! Published in 2019 at "American Political Science Review"
DOI: 10.1017/s0003055419000480
Abstract: Multilevel regression and post-stratification (MRP) is the current gold standard for extrapolating opinion data from nationally representative surveys to smaller geographic units. However, innovations in nonparametric regularization methods can further improve the researcher’s ability to… read more here.
Keywords: regression; bayesian additive; regression trees; opinion data ... See more keywords
Log-Linear Bayesian Additive Regression Trees for Multinomial Logistic and Count Regression Models
Sign Up to like & getrecommendations! Published in 2017 at "Journal of the American Statistical Association"
DOI: 10.1080/01621459.2020.1813587
Abstract: Abstract We introduce Bayesian additive regression trees (BART) for log-linear models including multinomial logistic regression and count regression with zero-inflation and overdispersion. BART has been applied to nonparametric mean regression and binary classification problems in… read more here.
Keywords: regression; multinomial logistic; regression trees; log linear ... See more keywords
Bayesian Additive Regression Trees (BART) with covariate adjusted borrowing in subgroup analyses
Sign Up to like & getrecommendations! Published in 2022 at "Journal of Biopharmaceutical Statistics"
DOI: 10.1080/10543406.2022.2089160
Abstract: ABSTRACT It is crucial in clinical trials to investigate treatment effect consistency across subgroups defined by patient baseline characteristics. However, there may be treatment effect variability across subgroups due to small subgroup sample size. Various… read more here.
Keywords: additive regression; trees bart; subgroup; bayesian additive ... See more keywords
Additive regression for predictors of various natures and possibly incomplete Hilbertian responses
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DOI: 10.1214/21-ejs1823
Abstract: Abstract: In this paper we consider a fully nonparametric additive regression model for responses and predictors of various natures. This includes the case of Hilbertian and incomplete (like censored or missing) responses, and continuous, nominal… read more here.
Keywords: various natures; predictors various; additive regression; discrete predictors ... See more keywords
Application of Bayesian Additive Regression Trees for Estimating Daily Concentrations of PM2.5 Components
Sign Up to like & getrecommendations! Published in 2020 at "Atmosphere"
DOI: 10.3390/atmos11111233
Abstract: Bayesian additive regression tree (BART) is a recent statistical method that combines ensemble learning and nonparametric regression. BART is constructed under a probabilistic framework that also allows for model-based prediction uncertainty quantification. We evaluated the… read more here.
Keywords: bart; daily concentrations; regression; pm2 components ... See more keywords
Forward Additive Regression for Ultrahigh Dimensional Nonparametric Additive Models
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DOI: 10.5705/ss.202017.0083
Abstract: Ultrahigh dimensional data are collected in many scientific fields where the predictor dimension is often much higher than the sample size. To reduce the ultrahigh dimensionality effectively, many marginal screening approaches are developed. However, existing… read more here.
Keywords: forward additive; regression; ultrahigh dimensional; additive models ... See more keywords