Robust designs for probability estimation in binary response experiments

Shih Hao Huang, Mong Na Lo Huang

Research output: Contribution to journalArticlepeer-review


The purpose of this work is to investigate robust design problems for estimation of the response probability curve under binary response experiments with model uncertainty consideration. A minimax type of model robust design criterion, called WB-optimum in short is proposed, based on minimization of the maximum of the weighted squared probability bias function under two rival models. The corresponding design issues are investigated and results under the above design criterion for given rival models with several commonly seen symmetric links are presented.

Original languageEnglish
Pages (from-to)116-132
Number of pages17
JournalJournal of Statistical Planning and Inference
Issue number1
StatePublished - 2014


  • Compromise weight functions
  • Equal oscillation
  • Logit model
  • Minimax optimality
  • Model discrimination
  • Probit model
  • WB-optimum


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