Maximum One-Factor-At-A-Time Designs for Screening in Computer Experiments. Issue 2 (3rd April 2023)

• Record Type: Journal Article
• Authors: Xiao, Qian; Joseph, V. Roshan; Ray, Douglas M.
• Is Part Of: Technometrics. Volume 65:Issue 2(2023)
• Page Start: 220
• Page End: 230
• Publication Date: 2023-04-03
• Subjects: Experimental design -- Morris screening -- Sensitivity analysis -- Sobol' indices; Statistical physics -- Periodicals; Chemistry -- Statistical methods -- Periodicals; Engineering -- Statistical methods -- Periodicals; 519.5
• DOI: 10.1080/00401706.2022.2141897
• Languages: English
• ISSNs: 0040-1706

Abstract

Abstract: 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.