Robust regression designs when the design space consists of finitely many points

DOI10.1214/aos/1176346406zbMath0546.62048OpenAlexW1963772992MaRDI QIDQ797941

Ker-Chau Li

Publication date: 1984

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1214/aos/1176346406

zbMATH Keywords

robust designleast squares estimatorssquared loss functiondesigns with finite supportmaximum risk among symmetric designsnearly linear regression

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Optimal statistical designs (62K05)

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Robust designs for approximately linear regression: \(M\)-estimated parameters ⋮ INTEGER-VALUED, MINIMAX ROBUST DESIGNS FOR APPROXIMATELY LINEAR MODELS WITH CORRELATED ERRORS ⋮ Model robust extrapolation designs ⋮ Optimal designs for regression models with possible bias ⋮ Robust designs for approximately polynomial regression ⋮ Restricted optimal designs for linear and quadratic polynomial regressions ⋮ Minimax designs for approximately linear regression ⋮ Minimax designs for linear regression models with bias in a reproducing kernel Hilbert space in a discrete set ⋮ Minimax regression designs for approximately linear models with autocorrelated errors

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