The Annals of Statistics

Optimal Weights for Experimental Designs on Linearly Independent Support Points

Friedrich Pukelsheim and Ben Torsney

Full-text: Open access

Abstract

An explicit formula is derived to compute the $A$-optimal design weights on linearly independent regression vectors, for the mean parameters in a linear model with homoscedastic variances. The formula emerges as a special case of a general result which holds for a wide class of optimality criteria. There are close links to iterative algorithms for computing optimal weights.

Article information

Source
Ann. Statist., Volume 19, Number 3 (1991), 1614-1625.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176348265

Digital Object Identifier
doi:10.1214/aos/1176348265

Mathematical Reviews number (MathSciNet)
MR1126341

Zentralblatt MATH identifier
0729.62063

JSTOR
links.jstor.org

Subjects
Primary: 62K05: Optimal designs

Keywords
General equivalence theorem information functions matrix means algorithms for optimal designs $A$-optimality $D$-optimality $c$-optimality

Citation

Pukelsheim, Friedrich; Torsney, Ben. Optimal Weights for Experimental Designs on Linearly Independent Support Points. Ann. Statist. 19 (1991), no. 3, 1614--1625. doi:10.1214/aos/1176348265. https://projecteuclid.org/euclid.aos/1176348265


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