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Activity Number: 616
Type: Contributed
Date/Time: Wednesday, August 3, 2016 : 2:00 PM to 3:50 PM
Sponsor: IMS
Abstract #318746 View Presentation
Title: D-Optimal Designs with Ordered Categorical Data
Author(s): Jie Yang* and Liping Tong and Abhyuday Mandal
Companies: University of Illinois at Chicago and Advocate Health Care and University of Georgia
Keywords: Approximate design ; Exact design ; Multinomial response ; Cumulative link model ; Minimally supported design ; Ordinal data
Abstract:

Cumulative link models have been widely used for ordered categorical responses. Uniform allocation of experimental units is commonly used in practice, but often suffers from a lack of efficiency. We consider D-optimal designs with ordered categorical responses and cumulative link models. For a predetermined set of design points, we derive the necessary and sufficient conditions for an allocation to be locally D-optimal and develop efficient algorithms for obtaining approximate and exact designs. We prove that the number of support points in a minimally supported design only depends on the number of predictors, which can be much less than the number of parameters in the model. We show that a D-optimal minimally supported allocation in this case is usually not uniform on its support points. In addition, we provide EW D-optimal designs as a highly efficient surrogate to Bayesian D-optimal designs. Both of them are much more robust than uniform designs.


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