Title: Model-robust and model-sensitive designs
Authors: Goos, Peter ×
Kobilinsky, A
O'Brien, TE
Vandebroek, Martina #
Issue Date: Apr-2005
Publisher: Elsevier science bv
Series Title: Computational statistics & data analysis vol:49 issue:1 pages:201-216
Abstract: The main drawback of the optimal design approach is that it assumes the statistical model is known. To overcome this problem, a new approach to reduce the dependency on the assumed model is proposed. The approach takes into account the model uncertainty by incorporating the bias in the design criterion and the ability to test for lack-of-fit. Several new designs are derived and compared to the alternatives available from the literature. (C) 2004 Elsevier B.V. All rights reserved.
ISSN: 0167-9473
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Research Center for Operations Research and Business Statistics (ORSTAT), Leuven
× corresponding author
# (joint) last author

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