The Annals of Statistics

Saturated locally optimal designs under differentiable optimality criteria

Linwei Hu, Min Yang, and John Stufken

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We develop general theory for finding locally optimal designs in a class of single-covariate models under any differentiable optimality criterion. Yang and Stufken [Ann. Statist. 40 (2012) 1665–1681] and Dette and Schorning [Ann. Statist. 41 (2013) 1260–1267] gave complete class results for optimal designs under such models. Based on their results, saturated optimal designs exist; however, how to find such designs has not been addressed. We develop tools to find saturated optimal designs, and also prove their uniqueness under mild conditions.

Article information

Ann. Statist., Volume 43, Number 1 (2015), 30-56.

First available in Project Euclid: 18 November 2014

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62K05: Optimal designs
Secondary: 62J02: General nonlinear regression

Chebyshev system complete class generalized linear model locally optimal design nonlinear model


Hu, Linwei; Yang, Min; Stufken, John. Saturated locally optimal designs under differentiable optimality criteria. Ann. Statist. 43 (2015), no. 1, 30--56. doi:10.1214/14-AOS1263.

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