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.

翻译：我们将经典Lasso与贝叶斯Lasso正则化方法应用于同时包含混合变量与过程变量的模型族。通过模拟研究与实际数据应用，我们分析了这些估计量相对于普通最小二乘估计器的性能表现。研究结果表明，贝叶斯Lasso（特别是通过坐标上升变分推断实现的方法）在变量选择精度与响应优化方面均展现出更优越的性能。