Lasso regularization for mixture experiments with noise variables

TL;DR

This paper compares classical and Bayesian lasso regularizations in mixture experiments with noise variables, demonstrating Bayesian lasso's superior performance in variable selection and response optimization through simulations and real data.

Contribution

It introduces the application of Bayesian lasso with coordinate ascent variational inference to mixture experiments with noise variables, showing improved results over traditional methods.

Findings

Bayesian lasso outperforms classical lasso in variable selection.

Bayesian approach achieves better response optimization.

Coordinate ascent variational inference enhances Bayesian lasso efficiency.

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.