Typically T-optimality is used to discriminate among several models with Normal errors. In order to discriminate between two non-Normal models, a criterion based on the Kullback-Liebler distance has been proposed, the so called KL-criterion. In this paper, a generalization of the KL-criterion is proposed to deal with discrimination among several non-Normal models. An example where three logistic regression models are compared is provided.
Optimal designs for discriminating among several non-normal models / C. Tommasi (CONTRIBUTIONS TO STATISTICS). - In: mODa 8 : advances in model-oriented design and analysis / [a cura di] J. Lòpez-Fidalgo, J.M. Rodrìguez-Dìaz, B. Torsney. - Heidelberg : Physica, 2007. - ISBN 978-3-7908-1951-9. - pp. 213-220 (( Intervento presentato al 8. convegno mODa: advances in model-oriented design and analysis tenutosi a Almagro nel 2007 [10.1007/978-3-7908-1952-6_27].
Optimal designs for discriminating among several non-normal models
C. TommasiPrimo
2007
Abstract
Typically T-optimality is used to discriminate among several models with Normal errors. In order to discriminate between two non-Normal models, a criterion based on the Kullback-Liebler distance has been proposed, the so called KL-criterion. In this paper, a generalization of the KL-criterion is proposed to deal with discrimination among several non-Normal models. An example where three logistic regression models are compared is provided.
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