Model discrimination-another perspective on model-robust designs

Bradley A. Jones, William Li, Christopher J. Nachtsheim, Qian K. Ye

Research output: Contribution to journalArticle

17 Citations (Scopus)

Abstract

Recent progress in model-robust designs has focused on maximizing estimation capacities. However, for a given design, two competing models may be both estimable and yet difficult or impossible to discriminate in the model selection procedure. In this paper, we propose several criteria for gauging the capability of a design for model discrimination. The criteria are then used to evaluate a class of 18-run orthogonal designs in terms of their model-discriminating capabilities. We demonstrate that designs having the same estimation capacity may differ considerably with respect to model-discrimination capabilities. The best designs according to the proposed model-discrimination criteria are obtained and tabulated for practical use.

Original languageEnglish (US)
Pages (from-to)1576-1583
Number of pages8
JournalJournal of Statistical Planning and Inference
Volume137
Issue number5
DOIs
StatePublished - May 1 2007

Fingerprint

Model Discrimination
Robust Design
Estimation Capacity
Orthogonal Design
Selection Procedures
Model Selection
Model
Gaging
Design
Robust design
Discrimination
Evaluate
Demonstrate

Keywords

  • Estimation capacity
  • Information capacity
  • Model discrimination
  • Model-robust design

ASJC Scopus subject areas

  • Statistics, Probability and Uncertainty
  • Applied Mathematics
  • Statistics and Probability

Cite this

Model discrimination-another perspective on model-robust designs. / Jones, Bradley A.; Li, William; Nachtsheim, Christopher J.; Ye, Qian K.

In: Journal of Statistical Planning and Inference, Vol. 137, No. 5, 01.05.2007, p. 1576-1583.

Research output: Contribution to journalArticle

Jones, Bradley A. ; Li, William ; Nachtsheim, Christopher J. ; Ye, Qian K. / Model discrimination-another perspective on model-robust designs. In: Journal of Statistical Planning and Inference. 2007 ; Vol. 137, No. 5. pp. 1576-1583.
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