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. (2007) Design selection criteria for discrimination/estimation for nested models and a binomial response [in special issue: International Conference on Design of Experiments (ICODOE)] Journal of Statistical Planning and Inference, 138, (1), pp. 132-144. (doi:10.1016/j.jspi.2007.05.017).


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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, TE-optimality, is defined, based on the difference in the deviances from the two models, and comparisons are made with T-, Ds- 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.

Item Type: Article
Digital Object Identifier (DOI): doi:10.1016/j.jspi.2007.05.017
ISSNs: 0378-3758 (print)
Keywords: binary response, deviance, D-optimality, Ds-optimality, hybrid designs, likelihood ratio test, T-optimality
ePrint ID: 151265
Date :
Date Event
13 May 2007e-pub ahead of print
1 January 2008Published
Date Deposited: 10 May 2010 09:55
Last Modified: 18 Apr 2017 04:21
Further Information:Google Scholar

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