Lasso regularization for mixture experiments with noise variables

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 t...

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Hauptverfasser:	González-Navarrete, Manuel, Manríquez-Méndez, Fabián, Pereira-Barahona, Manuel
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Sprache:	eng
Schlagworte:	Statistics - Applications Statistics - Methodology
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creator	González-Navarrete, Manuel Manríquez-Méndez, Fabián Pereira-Barahona, Manuel
description	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.
doi_str_mv	10.48550/arxiv.2406.12237
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title	Lasso regularization for mixture experiments with noise variables
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