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   Articles with "bayesian additive" as a keyword



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Bayesian additive regression trees for multivariate skewed responses

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recommendations! Published in 2022 at "Statistics in Medicine"

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
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Bayesian Additive Regression Trees using Bayesian model averaging

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recommendations! 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

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recommendations! 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
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Log-Linear Bayesian Additive Regression Trees for Multinomial Logistic and Count Regression Models

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recommendations! 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
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Bayesian Additive Regression Trees (BART) with covariate adjusted borrowing in subgroup analyses

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recommendations! 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
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Nonparametric failure time: Time-to-event machine learning with heteroskedastic bayesian additive regression trees and low information omnibus dirichlet process mixtures.

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recommendations! Published in 2023 at "Biometrics"

DOI: 10.1111/biom.13857

Abstract: Many popular survival models rely on restrictive parametric, or semi-parametric, assumptions that could provide erroneous predictions when the effects of covariates are complex. Modern advances in computational hardware have led to an increasing interest in… read more here.

Keywords: time event; time; failure time; bayesian additive ... See more keywords
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Application of Bayesian Additive Regression Trees for Estimating Daily Concentrations of PM2.5 Components

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recommendations! 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