A Random Forest Approach for Bounded Outcome Variables

DOI10.1080/10618600.2019.1705310OpenAlexW2995780113WikidataQ100250347 ScholiaQ100250347MaRDI QIDQ5066011

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Publication date: 28 March 2022

Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)

Full work available at URL: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8193767

zbMATH Keywords

random forestsbeta distributionregression modelingbounded outcome variables

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• GAMLSS
• ranger
• betareg

Cites Work

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• Beta Regression for Modelling Rates and Proportions
• Improved estimators for a general class of beta regression models
• Nonparametric additive beta regression for fractional response with application to body fat data
• Variable importance in binary regression trees and forests
• The logistic transform for bounded outcome scores
• Discrete-time survival trees
• Three-part model for fractional response variables with application to Chinese household health insurance coverage
• Discrete-time survival trees and forests with time-varying covariates
• Strictly Proper Scoring Rules, Prediction, and Estimation
• Generalized Additive Models for Location, Scale and Shape
• Random forests

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