Lasso regularization for mixture experiments with noise variables
Methodology
2024-06-19 v1 Applications
Abstract
We apply classical and Bayesian lasso regularizations to a family of models with the presence of mixture and process variables. We analyse the performance of these estimates with respect to ordinary least squares estimators by a simulation study and a real data application. Our results demonstrate the superior performance of Bayesian lasso, particularly via coordinate ascent variational inference, in terms of variable selection accuracy and response optimization.
Cite
@article{arxiv.2406.12237,
title = {Lasso regularization for mixture experiments with noise variables},
author = {Manuel González-Navarrete and Fabián Manríquez-Méndez and Manuel Pereira-Barahona},
journal= {arXiv preprint arXiv:2406.12237},
year = {2024}
}