Support points of locally optimal designs for nonlinear models with two parameters

Min Yang, John Stufken

Research output: Contribution to journalArticlepeer-review

56 Scopus citations


We propose a new approach for identifying the support points of a locally optimal design when the model is a nonlinear model. In contrast to the commonly used geometric approach, we use an approach based on algebraic tools. Considerations are restricted to models with two parameters, and the general results are applied to often used special cases, including logistic, probit, double exponential and double reciprocal models for binary data, a loglinear Poisson regression model for count data, and the Michaelis-Menten model. The approach, which is also of value for multi-stage experiments, works both with constrained and unconstrained design regions and is relatively easy to implement.

Original languageEnglish (US)
Pages (from-to)518-541
Number of pages24
JournalAnnals of Statistics
Issue number1
StatePublished - Feb 2009
Externally publishedYes


  • Binary response
  • Count data
  • Design of experiments
  • Generalized linear model
  • Loewner order
  • Michaelis-menten model
  • Multi-stage experiment
  • Optimality
  • Poisson model

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


Dive into the research topics of 'Support points of locally optimal designs for nonlinear models with two parameters'. Together they form a unique fingerprint.

Cite this