Premium
Techniques for the construction of robust regression designs
Author(s) -
Daemi Maryam,
Wiens Douglas P.
Publication year - 2013
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 - 10.1002/cjs.11194
Subject(s) - minimax , robust regression , regression , computer science , regression analysis , mathematics , outlier , mathematical optimization , statistics , algorithm
The authors review and extend the literature on robust regression designs. Even for straight line regression, there are cases in which the optimally robust designs—in a minimax mean squared error sense, with the maximum evaluated as the “true” model varies over a neighbourhood of that fitted by the experimenter—have not yet been constructed. They fill this gap in the literature, and in so doing introduce a method of construction that is conceptually and mathematically simpler than the sole competing method. The technique used injects additional insight into the structure of the solutions. In the cases that the optimality criteria employed result in designs that are not invariant under changes in the design space, their methods also allow for an investigation of the resulting changes in the designs. The Canadian Journal of Statistics 41: 679–695; 2013 © 2013 Statistical Society of Canada