Computing optimal approximate invariant designs for cubic regression on multidimensional balls and cubes

DOI10.1016/0378-3758(94)00140-QzbMath0848.62040OpenAlexW2041374156MaRDI QIDQ1907641

Berthold Heiligers, Norbert Gaffke

Publication date: 28 October 1996

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

Full work available at URL: https://doi.org/10.1016/0378-3758(94)00140-q

zbMATH Keywords

convex optimizationgradient methodsexact designsquasi-Newton methodsline searchrobust designssign changesgroup of permutationsmultiple polynomial regressionrotatable designsoptimal approximate designscubic multiple regressioninvariant criterionmixture criterionmultiple moments

Mathematics Subject Classification ID

Optimal statistical designs (62K05) Convex programming (90C25) Statistical tables (62Q05) Probabilistic methods, stochastic differential equations (65C99)

Related Items (10)

Characterizing D-optimal rotatable designs with finite reflection groups ⋮ On third order rotatability ⋮ Quasi-Newton algorithm for optimal approximate linear regression design: optimization in matrix space ⋮ Algorithms for optimal design with application to multiple polynomial regression ⋮ Unnamed Item ⋮ Optimal and robust invariant designs for cubic multiple regression ⋮ On optimal third order rotatable designs ⋮ A construction of the fourth order rotatable designs invariant under the hyperoctahedral group ⋮ Minimum support invariant designs for multiple cubic regression ⋮ Constructions of \(\Phi_p\)-optimal rotatable designs on the ball

Cites Work

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• A new approach to the construction of optimal designs
• Optimal and robust invariant designs for cubic multiple regression
• Measures of Lack of Fit for Response Surface Designs and Predictor Variable Transformations
• On a class of algorithms from experimental design theory
• Second order methods for solving extremum problems form optimal linar regression design
• Optimal and Efficient Designs of Experiments

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