D-optimal Designs with Ordered Categorical Data

Jie Yang; Liping Tong; Abhyuday Mandal

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arXiv · 1502.05990

D-optimal Designs with Ordered Categorical Data

Jie Yang·Liping Tong·Abhyuday Mandal

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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 can be much more robust than uniform designs.

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Jie Yang, Liping Tong, Abhyuday Mandal. 2015-02-20. D-optimal Designs with Ordered Categorical Data. https://arxiv.org/abs/1502.05990

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