Model discrimination-another perspective on model-robust designs

Bradley A. Jones, William Li, Christopher J. Nachtsheim, Kenny Q. Ye

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

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

Bibliographical note

Funding Information:
This research was supported by the Supercomputing Institute for Digital Simulation and Advanced Computation at the University of Minnesota. The research of William Li and Christopher J. Nachtsheim is supported by the Research and Teaching Supplements System in Carlson School of Management at the University of Minnesota. The research of Kenny Q. Ye is supported in part by National Science Foundation grant DMS-0306306. We thank the editor and two referees for the helpful comments.

Keywords

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

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