The Annals of Statistics

Saturated locally optimal designs under differentiable optimality criteria

Linwei Hu, Min Yang, and John Stufken

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Abstract

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

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

Dates
First available in Project Euclid: 18 November 2014

Permanent link to this document
https://projecteuclid.org/euclid.aos/1416322035

Digital Object Identifier
doi:10.1214/14-AOS1263

Mathematical Reviews number (MathSciNet)
MR3285599

Zentralblatt MATH identifier
1321.62097

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

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

Citation

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. https://projecteuclid.org/euclid.aos/1416322035


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