Goos, Peter

Strouwen, Arno

Van Brantegem, Karel

Abstract: In various kinds of industries, researchers conduct experiments in which the experimental factors affect an ordinal outcome with three or more categories. A popular model for ordinal outcome variables is the cumulative logit model which is also known as the proportional odds model. In this article, we explore locally and Bayesian D-and I-optimal experimental designs for the cumulative logit model. We perform an instructive sensitivity study to learn about the dependency of D-and I-optimal designs on the values of the model parameters and on the number of outcome categories, and use a polypropylene experiment as a proof-of-concept example

D- and I-optimal design of multi-factor industrial experiments with ordinal outcomes

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