BiMM tree: a decision tree method for modeling clustered and longitudinal binary outcomes

DOI10.1080/03610918.2018.1490429OpenAlexW2892216367WikidataQ94592535 ScholiaQ94592535MaRDI QIDQ5088020

Jaime Lynn Speiser, Dongjun Chung, Valerie L. Durkalski, David Koch, Constantine J. Karvellas, Bethany J. Wolf

Publication date: 4 July 2022

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://europepmc.org/articles/pmc7202553

zbMATH Keywords

longitudinal dataclustered datadecision treeclassification and regression treemixed effects

Mathematics Subject Classification ID

Software, source code, etc. for problems pertaining to statistics (62-04)

Related Items (2)

On missing random effects in machine learning ⋮ Machine learning and design of experiments with an application to product innovation in the chemical industry

Uses Software

• R
• lme4
• bartMachine
• blme
• BartPy
• MMI_IMPUTE

Cites Work

• Unnamed Item
• BART: Bayesian additive regression trees
• RE-EM trees: a data mining approach for longitudinal and clustered data
• Mixed effects regression trees for clustered data
• On generalized multivariate decision tree by using GEE
• Multivariate trees for mixed outcomes
• A weakly informative default prior distribution for logistic and other regression models
• Regression trees for longitudinal and multiresponse data
• Bayesian inference for generalized linear mixed models
• Fifty Years of Classification and Regression Trees
• Mixed-effects random forest for clustered data
• Prediction with missing data via Bayesian Additive Regression Trees
• Nonparametric Regression Methods for Longitudinal Data Analysis
• The elements of statistical learning. Data mining, inference, and prediction

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