Optimal designs and large-sample tests for linear hypotheses

D. R. Jensen, L. S. Mayer, R. H. Mayers

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Moment conditions beyond those required for Gauss-Markov estimation are shown to yield error bounds on normal-theory approximations to type I error probabilities and confidence coefficients associated with variance ratio tests, Scheffé's (1953) bounds, and Dunnett's (1955) procedure for comparing k treatments with a control. These bounds depend on the experimental design. The error-minimizing designs are characterized and shown to be orthogonal.

Original languageEnglish (US)
Pages (from-to)71-78
Number of pages8
Issue number1
StatePublished - Apr 1975


  • Berry-Esséen bounds
  • Central limit theory
  • Dunnett's procedure
  • Linear models
  • Optimal designs and robustness
  • Scheffé's projections

ASJC Scopus subject areas

  • Statistics and Probability
  • Mathematics(all)
  • Agricultural and Biological Sciences (miscellaneous)
  • Agricultural and Biological Sciences(all)
  • Statistics, Probability and Uncertainty
  • Applied Mathematics


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