Open Access
February 2009 Support points of locally optimal designs for nonlinear models with two parameters
Min Yang, John Stufken
Ann. Statist. 37(1): 518-541 (February 2009). DOI: 10.1214/07-AOS560


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.


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Min Yang. John Stufken. "Support points of locally optimal designs for nonlinear models with two parameters." Ann. Statist. 37 (1) 518 - 541, February 2009.


Published: February 2009
First available in Project Euclid: 16 January 2009

zbMATH: 1155.62053
MathSciNet: MR2488361
Digital Object Identifier: 10.1214/07-AOS560

Primary: 62K05
Secondary: 62J12

Keywords: Binary response , count data , Design of experiments , generalized linear model , Loewner order , Michaelis–Menten model , multi-stage experiment , optimality , Poisson model

Rights: Copyright © 2009 Institute of Mathematical Statistics

Vol.37 • No. 1 • February 2009
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