首页|D- and I-optimal design of multi-factor industrial experiments with ordinal outcomes
D- and I-optimal design of multi-factor industrial experiments with ordinal outcomes
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NSTL
Elsevier
? 2021In 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.
Bayesian experimental designCumulative logit modelD- and I-optimalityMultinomial regression modelOrdinal dataPolypropylene experimentProportional odds model
Van Brantegem K.、Strouwen A.、Goos P.
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Faculty of Bioscience Engineering and Leuven Statistics Research Centre
NSTL
Elsevier
