Experimental design for binary data

K. M. Abdelbasit, R. L. Plackett

Research output: Contribution to journalArticlepeer-review

163 Citations (Scopus)


Models for binary data are usually such that the information matrix depends on the unknown parameters. Thus the standard criteria for optimality in regression experiments cannot be applied without modification. Methods of going around this difficulty include the use of initial point estimates, sequential methods, and Bayesian analysis. This article is mainly concerned with the robustness and the number of design points for methods involving initial estimates, and for sequential methods in a small number of stages. A final section discusses the criterion of constant information for models involving one or two parameters, and summarizes recent results in this field.

Original languageEnglish
Pages (from-to)90-98
Number of pages9
JournalJournal of the American Statistical Association
Issue number381
Publication statusPublished - Mar 1983
Externally publishedYes


  • Binary data
  • Constant information
  • D-optimality
  • Fiducial method
  • Robustness
  • Sequential method

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


Dive into the research topics of 'Experimental design for binary data'. Together they form a unique fingerprint.

Cite this