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Activity Number: 383
Type: Contributed
Date/Time: Tuesday, August 2, 2016 : 11:35 AM to 12:20 PM
Sponsor: Biopharmaceutical Section
Abstract #321624
Title: D-Optimal Designs for Multinomial Logistic Models
Author(s): Xianwei Bu* and Jie Yang
Companies: UIC and University of Illinois at Chicago
Keywords: Multinomial response ; Generalized linear model ; Proportional odds ; Fisher information matrix ; , Minimally supported design ; Lift-one algorithm
Abstract:

Multinomial logistic models have been widely used for categorical responses or multinomial responses, including baseline-category logit model for nominal responses, cumulative logit model and adjacent-categories logit model for ordinal responses, and continuation-ratio logit model for hierarchical responses. In order to construct a general framework towards D-optimal designs for these models, we unify all the four models into a common form and extend them to fit different model assumptions, including proportional odds (po), non-proportional odds (npo), and partial proportional odds (ppo). We explore the design space of these models, derive a simplified form of the Fisher information matrix, and obtain explicit formulas of its determinant. We derive necessary and sufficient conditions for a minimally supported design to be D-optimal. We also develop efficient numerical algorithms for searching D-optimal designs.


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