The Annals of Statistics

Optimal discrimination designs for multifactor experiments

Holger Dette and Ingo Röder

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Abstract

In this paper efficient designs are determined when Anderson's procedure is applied in order to identify the degree of a multivariate polynomial regression model. It is shown that the optimal designs are very closely related to model robust designs which maximize a weighted p-mean of D-efficiencies. As a consequence we obtain designs with high efficiency for model discrimination and for the statistical analysis in the identified model.

Article information

Source
Ann. Statist., Volume 25, Number 3 (1997), 1161-1175.

Dates
First available in Project Euclid: 20 November 2003

Permanent link to this document
https://projecteuclid.org/euclid.aos/1069362742

Digital Object Identifier
doi:10.1214/aos/1069362742

Mathematical Reviews number (MathSciNet)
MR1447745

Zentralblatt MATH identifier
0888.62077

Subjects
Primary: 62K05: Optimal designs
Secondary: 62G10: Hypothesis testing

Keywords
Multifactor experiments model discrimination optimal designs invariance

Citation

Dette, Holger; Röder, Ingo. Optimal discrimination designs for multifactor experiments. Ann. Statist. 25 (1997), no. 3, 1161--1175. doi:10.1214/aos/1069362742. https://projecteuclid.org/euclid.aos/1069362742


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