Predictive Response Surface Models: To Reduce or Not to Reduce?

• Activity Number: 586 - Recent Developments in Designs of Experiments and Responses Surface Models
• Type: Contributed
• Date/Time: Wednesday, August 1, 2018 : 2:00 PM to 3:50 PM
• Sponsor: Section on Physical and Engineering Sciences
• Author(s): Byran Smucker* and Maria Weese and David J Edwards
• Companies: Miami University and Miami University and Virginia Commonwealth University

Abstract

In classical response surface methodology (RSM) the final, optimization step uses a small number of factors that clearly drive the process under study. However, in practice sometimes experimenters fit a second-order model without having done much previous experimentation. In this case, the true model is particularly uncertain and using the full model may lead to overfitting. In this study, we obtain 25 responses from 12 response surface studies from the literature, each of which include published validation runs. We then analyze the original RSM experiments under several strategies, including the full second-order model, reducing via p-values, forward selection, and the Lasso. We then compare the predictions for these various methods with the actual validation responses to determine which method was most predictive. We study the same analysis methods using simulated data as well.