Locally D-Optimal Designs for Binary Responses and Multiple Continuous Design Variables

Zhongshen Wang () and John Stufken ()
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Zhongshen Wang: Apellis Pharmaceuticals, Inc.
John Stufken: University of North Carolina at Greensboro

Journal of Quantitative Economics, 2022, vol. 20, issue 1, No 6, 113 pages

Abstract: Abstract We identify locally D-optimal designs for binary data when a generalized linear model with multiple continuous covariates whose values can be selected at the design stage. Yang et al. (Stat Sin 21:1415–1430, 2011) provided an explicit form for D-optimal designs when there are no interaction effects between the design variables. After providing an alternative proof of that result, we generalize the result by identifying D-optimal designs for models with interactions between the design variables that satisfy the strong effect heredity principle. We also employ orthogonal arrays to obtain more practical D-optimal designs with a smaller support size.

Keywords: Locally optimal design; D-optimality; Multiple covariates; Equivalence theorem; Orthogonal arrays (search for similar items in EconPapers)
JEL-codes: C02 C18 C90 (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s40953-022-00304-z

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