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Restricted minimax robust designs for misspecified regression models
Author(s) -
Heo Giseon,
Schmuland Byron,
Wiens Douglas P.
Publication year - 2001
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.2307/3316055
Subject(s) - minimax , robustness (evolution) , parametric statistics , mathematics , polynomial regression , class (philosophy) , mathematical optimization , regression , polynomial , regression analysis , computer science , statistics , artificial intelligence , biochemistry , chemistry , mathematical analysis , gene
The authors propose and explore new regression designs. Within a particular parametric class, these designs are minimax robust against bias caused by model misspecification while attaining reasonable levels of efficiency as well. The introduction of this restricted class of designs is motivated by a desire to avoid the mathematical and numerical intractability found in the unrestricted minimax theory. Robustness is provided against a family of model departures sufficiently broad that the minimax design measures are necessarily absolutely continuous. Examples of implementation involve approximate polynomial and second order multiple regression.