Design selection criteria for discrimination/estimation for nested models and a binomial response

Waterhouse, T. H., Woods, D. C., Eccleston, J. A. and Lewis, S. M. (2008) Design selection criteria for discrimination/estimation for nested models and a binomial response. Journal of Statistical Planning and Inference, 138 1: 132-144. doi:10.1016/j.jspi.2007.05.017


Author Waterhouse, T. H.
Woods, D. C.
Eccleston, J. A.
Lewis, S. M.
Title Design selection criteria for discrimination/estimation for nested models and a binomial response
Journal name Journal of Statistical Planning and Inference   Check publisher's open access policy
ISSN 0378-3758
1873-1171
Publication date 2008-01-01
Year available 2008
Sub-type Article (original research)
DOI 10.1016/j.jspi.2007.05.017
Open Access Status
Volume 138
Issue 1
Start page 132
End page 144
Total pages 13
Place of publication Amsterdam, The Netherlands
Publisher Elsevier BV * North-Holland
Language eng
Subject 1804 Statistics, Probability and Uncertainty
2604 Applied Mathematics
2613 Statistics and Probability
Abstract The aim of an experiment is often to enable discrimination between competing forms for a response model. We investigate the selection of a continuous design for a non-sequential strategy when there are two competing generalized linear models for a binomial response, with a common link function and the linear predictor of one model nested within that of the other. A new criterion, T E-optimality, is defined, based on the difference in the deviances from the two models, and comparisons are made with T-, D s- and D-optimality. Issues are raised through the study of two examples in which designs are assessed using simulation studies of the power to reject the null hypothesis of the smaller model being correct, when the data are generated from the larger model. Parameter estimation for discrimination designs is also discussed and a simple method is investigated of combining designs to form a hybrid design in order to achieve both model discrimination and estimation. This method has a computational advantage over the use of a compound criterion and the similar performance of the designs obtained from the two approaches is illustrated in an example.
Keyword Binary response
Deviance
Hybrid designs
Likelihood ratio test
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: School of Medicine Publications
 
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