First-order optimal designs for non-linear models
UNSPECIFIED (1998) First-order optimal designs for non-linear models. JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 74 (1). pp. 177-192. ISSN 0378-3758Full text not available from this repository.
This paper presents D-optimal experimental designs for a variety of non-linear models which depend on an arbitrary number of covariates but assume a positive prior mean and a Fisher information matrix satisfying particular properties. It is argued that these optimal designs can be regarded as a first-order approximation of the asymptotic increase of Shannon information. The efficiency of this approximation is compared in some examples, which show how the results can be further used to compute the Bayesian optimal design, when the approximate solution is not accurate enough. (C) 1998 Elsevier Science B.V. All rights reserved.
|Item Type:||Journal Article|
|Subjects:||Q Science > QA Mathematics|
|Journal or Publication Title:||JOURNAL OF STATISTICAL PLANNING AND INFERENCE|
|Publisher:||ELSEVIER SCIENCE BV|
|Date:||1 October 1998|
|Number of Pages:||16|
|Page Range:||pp. 177-192|
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