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On missing random effects in machine learning

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Publication:5055126
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DOI10.1080/03610918.2020.1801729OpenAlexW3048463499MaRDI QIDQ5055126

Wenzhao Yang, Fabio D'Ottaviano

Publication date: 13 December 2022

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

Full work available at URL: https://doi.org/10.1080/03610918.2020.1801729


zbMATH Keywords

simulationrandom effectsartificial neural networkmixed effects modelsimulation design


Mathematics Subject Classification ID

Statistics (62-XX)




Cites Work

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  • RE-EM trees: a data mining approach for longitudinal and clustered data
  • Mixed effects regression trees for clustered data
  • Unbiased regression trees for longitudinal and clustered data
  • Generalized mixed effects regression trees
  • Design and analysis of mixture experiments with process variable
  • Mixture experiments with process variables: d-optimal orthogonal experimental designs
  • A new model and class of designs for mixture experiments with process variables
  • BiMM tree: a decision tree method for modeling clustered and longitudinal binary outcomes
  • Mixed-effects random forest for clustered data


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