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D-OPTIMAL DESIGNS WITH ORDERED CATEGORICAL DATA

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Title: D-OPTIMAL DESIGNS WITH ORDERED CATEGORICAL DATA
Author(s): Yang, J; Tong, LP; Mandal, A
Subject(s): Approximate design; cumulative link model; exact design; minimally supported design; multinomial response; 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 can be much more robust than uniform designs.
Issue Date: 2017-10
Publisher: STATISTICA SINICA
Citation Info: Yang, J., Tong, L. P. and Mandal, A. D-OPTIMAL DESIGNS WITH ORDERED CATEGORICAL DATA. Statistica Sinica. 2017. 27(4): 1879-1902. 10.5705/ss.202016.0210.
Type: Article
Description: "This is a copy of an article published in the STATISTICA SINICA © 2017 STATISTICA SINICA"
URI: http://hdl.handle.net/10027/22113
ISSN: 1017-0405
Date Available in INDIGO: 2017-11-10
 

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