Machine learning based on extended generalized linear model applied in mixture experiments

DOI10.1080/03610918.2019.1697821OpenAlexW2995037200WikidataQ126545662 ScholiaQ126545662MaRDI QIDQ5082996

Gilberto Rodrigues Liska, Marcelo Angelo Cirillo, Júlio Sílvio de Sousa Bueno Filho, Fortunato Silva de Menezes

Publication date: 21 June 2022

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

Full work available at URL: https://doi.org/10.1080/03610918.2019.1697821

zbMATH Keywords

machine learningdispersion modelsimplex spaceboosting algorithmregression modeling

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• R
• mboost
• aplore3
• bbmle
• betareg
• hnp
• mixexp

Cites Work

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• Greedy function approximation: A gradient boosting machine.
• Model-based boosting in R: a hands-on tutorial using the R package mboost
• Beta Regression for Modelling Rates and Proportions
• Boosting algorithms: regularization, prediction and model fitting
• Additive logistic regression: a statistical view of boosting. (With discussion and a rejoinder by the authors)
• Akaike's information criterion and recent developments in information complexity
• Response Surfaces, Mixtures, and Ridge Analyses
• Boosting With theL₂Loss
• Applied Logistic Regression
• Regression analysis of proportional data using simplex distribution
• Alternative modeling techniques for the quantal response data in mixture experiments

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