Polars and subgradients of mixtures of information functions

DOI10.1016/0378-3758(93)90070-MzbMath0770.62056MaRDI QIDQ2365871

Susanne Gutmair

Publication date: 29 June 1993

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

zbMATH Keywords

convex optimizationlinear modelequivalence theoremmoment matrix\(D\)-optimalitysubgradients\(A\)-optimalityboundary pointsapproximate design theorypolar functionshomoscedastic quadraticmixtures of information functionsmultipurpose designreal-valued optimality criteria

Mathematics Subject Classification ID

Optimal statistical designs (62K05) Design of statistical experiments (62K99) Applications of mathematical programming (90C90)

Cites Work

• A generalization of D- and \(D_ 1\)-optimal designs in polynomial regression
• General differential and Lagrangian theory for optimal experimental design
• Optimal designs for trigonometric and polynomial regression using canonical moments
• On linear regression designs which maximize information
• General equivalence theory for optimum designs (approximate theory)
• On information functions and their polars
• Directional derivatives of optimality criteria at singular matrices in convex design theory
• Model Robust, Linear-Optimal Designs
• Experimental design in a class of models
• Optimal multipurpose designs for regression models
• On a class of algorithms from experimental design theory
• Another Proof that Convex Functions are Locally Lipschitz
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