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A note on maximin and Bayesian D-optimal designs in weighted polynomial regression

A note on maximin and Bayesian D-optimal designs in weighted polynomial regression
A note on maximin and Bayesian D-optimal designs in weighted polynomial regression
We consider the problem of finding D-optimal designs for estimating the coefficients in a weighted polynomial regression model with a certain efficiency function depending on two unknown parameters, which models the heteroscedastic error structure. This problem is tackled by adopting a Bayesian and a maximin approach, and optimal designs supported on a minimal number of support points are determined explicitly.
maximin optimality, bayesian optimal designs, efficiency function, parameter estimation, Jacobi polynomials
358-370
Biedermann, Stefanie
fe3027d2-13c3-4d9a-bfef-bcc7c6415039
Dette, Holger
8c7b1c2e-3adc-45df-acfc-9e76509a228e
Biedermann, Stefanie
fe3027d2-13c3-4d9a-bfef-bcc7c6415039
Dette, Holger
8c7b1c2e-3adc-45df-acfc-9e76509a228e

Biedermann, Stefanie and Dette, Holger (2003) A note on maximin and Bayesian D-optimal designs in weighted polynomial regression. Mathematical Methods of Statistics, 12 (3), 358-370.

Record type: Article

Abstract

We consider the problem of finding D-optimal designs for estimating the coefficients in a weighted polynomial regression model with a certain efficiency function depending on two unknown parameters, which models the heteroscedastic error structure. This problem is tackled by adopting a Bayesian and a maximin approach, and optimal designs supported on a minimal number of support points are determined explicitly.

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Published date: 2003
Keywords: maximin optimality, bayesian optimal designs, efficiency function, parameter estimation, Jacobi polynomials
Organisations: Statistics

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Local EPrints ID: 41835
URI: http://eprints.soton.ac.uk/id/eprint/41835
PURE UUID: 1052fe98-e24e-484c-ab08-f3d57fe079c1
ORCID for Stefanie Biedermann: ORCID iD orcid.org/0000-0001-8900-8268

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Date deposited: 10 Oct 2006
Last modified: 12 Dec 2021 03:35

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Contributors

Author: Holger Dette

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