Efficient geometric and uniform design strategies for sigmoidal regression models

• Main Authors: Timothy E. O'Brien, Suree Chooprateep, Nontiya Homkham
• Format: Journal
• Published: 2018
• Subjects: Decision Sciences; Mathematics
• Online Access: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=67650136138&origin=inward; http://cmuir.cmu.ac.th/jspui/handle/6653943832/49025
• Institution: Chiang Mai University

This paper provides practical guidelines for choosing efficient geometric and uniform designs for the logistic class of dose-response bioassay model functions in both the homoskedastic Gaussian and Binomial settings. The efficiencies of the designs provided here are typically above 90%, and since the number of design support points generally exceeds the number of parameters, these designs provide a useful and efficient means to confirm the assumed model. Extensions of our basic strategy include a Bayesian maxi-min design approach to reflect a range of values of the initial parameter estimates, as well as geometric/uniform design analogues when uncertainty exists as to the correct scale or to take account of curvature.