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Titolo:
Two robust design approaches for linear models with correlated errors
Autore:
Zhou, J;
Indirizzi:
Univ Victoria, Dept Math & Stat, Victoria, BC V8W 3P4, Canada Univ Victoria Victoria BC Canada V8W 3P4 at, Victoria, BC V8W 3P4, Canada
Titolo Testata:
STATISTICA SINICA
fascicolo: 1, volume: 11, anno: 2001,
pagine: 261 - 272
SICI:
1017-0405(200101)11:1<261:TRDAFL>2.0.ZU;2-R
Fonte:
ISI
Lingua:
ENG
Soggetto:
MINIMAX DESIGNS; REGRESSION;
Keywords:
approximately linear regression response; infinitesimal approach; M-robust design; minimax design; moving average errors; robust regression design;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Physical, Chemical & Earth Sciences
Citazioni:
11
Recensione:
Indirizzi per estratti:
Indirizzo: Zhou, J Univ Victoria, Dept Math & Stat, Victoria, BC V8W 3P4, Canada UnivVictoria Victoria BC Canada V8W 3P4 oria, BC V8W 3P4, Canada
Citazione:
J. Zhou, "Two robust design approaches for linear models with correlated errors", STAT SINICA, 11(1), 2001, pp. 261-272

Abstract

In this paper, infinitesimal and minimax approaches are used to construct robust regression designs for linear models with correlated errors. We consider IMSE (Integrated Mean Squared Error) as the loss function. Using an infinitesimal approach, we minimize IMSE at the ideal model subject to two robust constraints to derive M-robust designs. We also minimize the maximum of the IMSE to obtain minimax designs. In particular, M-robust and minimax designs are constructed for an approximately linear model with MA(1) errors. These designs are robust against small departures from the assumed regression response and small departures from the assumption of uncorrelated errors. It is interesting that M-robust and minimax designs have the same farm of density function, while M-robust designs require less restrictive ordering of design points. Implementation is discussed and examples are given.

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Documento generato il 29/09/20 alle ore 10:28:30