  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.
