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On optimal designs for nonlinear models: a general and efficient algorithm

On optimal designs for nonlinear models: a general and efficient algorithm
On optimal designs for nonlinear models: a general and efficient algorithm
Deriving optimal designs for nonlinear models is challenging in general. Although some recent results allow us to focus on a simple subclass of designs for most problems, deriving a specific optimal design mainly depends on algorithmic approaches. There is need of a general and efficient algorithm which is more broadly applicable than the current state of the art methods. We present a new algorithm that can be used to find optimal designs with respect to a broad class of optimality criteria, when the model parameters or functions thereof are of interest, and for both locally optimal and multi- stage design strategies. We prove convergence to the desired optimal design, and show that the new algorithm outperforms the best available algorithm in various examples
convergence, locally optimal design, multi-stage design, \Phi p -optimality
0162-1459
1411-1420
Yang, Min
df9fc4d0-03bd-47f1-94e0-1481f28a6f55
Biedermann, Stefanie
fe3027d2-13c3-4d9a-bfef-bcc7c6415039
Tang, Elina
15728b76-28c3-49ca-9036-8dc6ef959a37
Yang, Min
df9fc4d0-03bd-47f1-94e0-1481f28a6f55
Biedermann, Stefanie
fe3027d2-13c3-4d9a-bfef-bcc7c6415039
Tang, Elina
15728b76-28c3-49ca-9036-8dc6ef959a37

Yang, Min, Biedermann, Stefanie and Tang, Elina (2013) On optimal designs for nonlinear models: a general and efficient algorithm. Journal of the American Statistical Association, 108 (504), 1411-1420. (doi:10.1080/01621459.2013.806268).

Record type: Article

Abstract

Deriving optimal designs for nonlinear models is challenging in general. Although some recent results allow us to focus on a simple subclass of designs for most problems, deriving a specific optimal design mainly depends on algorithmic approaches. There is need of a general and efficient algorithm which is more broadly applicable than the current state of the art methods. We present a new algorithm that can be used to find optimal designs with respect to a broad class of optimality criteria, when the model parameters or functions thereof are of interest, and for both locally optimal and multi- stage design strategies. We prove convergence to the desired optimal design, and show that the new algorithm outperforms the best available algorithm in various examples

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More information

Published date: 2013
Keywords: convergence, locally optimal design, multi-stage design, \Phi p -optimality
Organisations: Statistics

Identifiers

Local EPrints ID: 337088
URI: http://eprints.soton.ac.uk/id/eprint/337088
ISSN: 0162-1459
PURE UUID: 35963335-870d-4c5d-abe3-c571a2513cc7
ORCID for Stefanie Biedermann: ORCID iD orcid.org/0000-0001-8900-8268

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Date deposited: 19 Apr 2012 13:22
Last modified: 09 Jan 2022 03:22

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Contributors

Author: Min Yang
Author: Elina Tang

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