The Annals of Statistics

E-Optimal Designs for Polynomial Regression

Friedrich Pukelsheim and William J. Studden

Full-text: Open access

Abstract

E-optmal designs for the full mean parameter vector, and for many subsets in univariate polynomial regression models are determined. The derivation is based on the interplay between E-optimality and scalar optimality. The scalar parameter systems are obtained as transformations of the coefficient vector c of the Chebyshev polynomial.

Article information

Source
Ann. Statist., Volume 21, Number 1 (1993), 402-415.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176349033

Digital Object Identifier
doi:10.1214/aos/1176349033

Mathematical Reviews number (MathSciNet)
MR1212184

Zentralblatt MATH identifier
0787.62075

JSTOR
links.jstor.org

Subjects
Primary: 62K05: Optimal designs

Keywords
Approximate design theory Chebyshev polynomial c-optimality E-optimality parameter subset optimality polynomial regression

Citation

Pukelsheim, Friedrich; Studden, William J. E-Optimal Designs for Polynomial Regression. Ann. Statist. 21 (1993), no. 1, 402--415. doi:10.1214/aos/1176349033. https://projecteuclid.org/euclid.aos/1176349033


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