Parameter neutral optimum design for non-linear models

D. Firth; J. P. Hinde

doi:10.1111/1467-9868.00097

Parameter neutral optimum design for non-linear models

D. Firth
, J. P. Hinde

Research output: Contribution to a Journal (Peer & Non Peer) › Article › peer-review

11 Citations (Scopus)

Abstract

Some Bayesian approaches to D-optimum design of experiments are considered from the viewpoint of invariance under reparameterization of the underlying statistical model. An invariant criterion is proposed which does not require the detailed specification of a prior, and which is shown to be equivalent to G-optimality under a Jeffreys prior. The methods are applied and discussed in the contexts of exponential decay and quantal response models.

Original language	English
Pages (from-to)	799-811
Number of pages	13
Journal	Journal of the Royal Statistical Society. Series B: Statistical Methodology
Volume	59
Issue number	4
DOIs	https://doi.org/10.1111/1467-9868.00097
Publication status	Published - 1997
Externally published	Yes

Keywords

Bayesian design
D-optimality
Equivalence theorem
G-optimality
Invariance
Jeffreys prior
Logistic regression
Non-linear model

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10.1111/1467-9868.00097

Cite this

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KW - Bayesian design

KW - D-optimality

KW - Equivalence theorem

KW - G-optimality

KW - Invariance

KW - Jeffreys prior

KW - Logistic regression

KW - Non-linear model

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JF - Journal of the Royal Statistical Society. Series B: Statistical Methodology

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Parameter neutral optimum design for non-linear models

Abstract

Keywords

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