Maximum One-Factor-At-A-Time Designs for Screening in Computer Experiments

Identifying important factors from a large number of potentially important factors of a highly nonlinear and computationally expensive black box model is a difficult problem. Morris screening and Sobol' design are two commonly used model-free methods for doing this. In this article, we establish a connection between these two seemingly different methods in terms of their underlying experimental design structure and further exploit this connection to develop an improved design for screening called Maximum One-Factor-At-A-Time (MOFAT) design. We also develop efficient methods for constructing MOFAT designs with a large number of factors. Several examples are presented to demonstrate the advantages of MOFAT designs compared to Morris screening and Sobol' design methods.

Automated summary

This article explores the difficult problem of identifying important factors from a large number of potentially important factors in a highly nonlinear and computationally expensive black box model. By establishing a connection between Morris screening and Sobol' design, an improved design called MOFAT is developed for screening, along with efficient methods for constructing MOFAT designs with a large number of factors.