Directional derivatives of optimality criteria at singular matrices in convex design theory
From MaRDI portal
Publication:3707172
DOI10.1080/02331888508801868zbMath0584.62116OpenAlexW2044185384MaRDI QIDQ3707172
Publication date: 1985
Published in: Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/02331888508801868
minimax theoremssubgradientssingular information matricesshorted operatorsdirectional derivatives of convex optimality criteriageneral equivalence theorem for optimalityminimum semi-norm generalized inverses
Related Items (8)
\(G\)-optimal designs for multi-factor experiments with heteroscedastic errors ⋮ Polars and subgradients of mixtures of information functions ⋮ On conjugate functions, subgradients, and directional derivatives of a class of optimality criteria in experimental design ⋮ Subdifferentiability and Lipschitz continuity in experimental design problems ⋮ Once more: optimal experimental design for regression models (with discussion) ⋮ On a class of algorithms from experimental design theory ⋮ Gustav Elfving's contribution to the emergence of the optimal experimental design theory. ⋮ A new interpretation of optimality for \(E\)-optimal designs in linear regression models
Cites Work
This page was built for publication: Directional derivatives of optimality criteria at singular matrices in convex design theory