Optimal and efficient crossover designs for comparing test treatments to a control treatment under various models

Min Yang, John Stufken

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

We study the optimality, efficiency, and robustness of crossover designs for comparing several test treatments to a control treatment. Since A-optimality is a natural criterion in this context, we establish lower bounds for the trace of the inverse of the information matrix for the test treatments versus control comparisons under various models. These bounds are then used to obtain lower bounds for efficiencies of a design under these models. Two algorithms, both guided by these efficiencies and results from optimal design theory, are proposed for obtaining efficient designs under the various models.

Original languageEnglish (US)
Pages (from-to)278-285
Number of pages8
JournalJournal of Statistical Planning and Inference
Volume138
Issue number1
DOIs
StatePublished - Jan 1 2008
Externally publishedYes

Fingerprint

Crossover Design
A-optimality
Lower bound
Information Matrix
Optimality
Trace
Model
Robustness
Crossover
Design
Lower bounds

Keywords

  • A-optimal designs
  • Balanced designs
  • Carryover effect
  • Crossover designs
  • Repeated measurements

ASJC Scopus subject areas

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

Cite this

Optimal and efficient crossover designs for comparing test treatments to a control treatment under various models. / Yang, Min; Stufken, John.

In: Journal of Statistical Planning and Inference, Vol. 138, No. 1, 01.01.2008, p. 278-285.

Research output: Contribution to journalArticle

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