Bayesian optimal designs for discriminating between non-Normal models
Chiara Tommasi, University of Milano
Jesus Lopez Fidalgo, University of Castilla La Mancha (Spain)
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ABSTRACT:
Designs are found for discriminating between two non-Normal models in the presence of prior information.
The KL-optimality criterion, where the true model is assumed to be completely known, is extended to a criterion where
prior distributions of the parameters and a prior probability of each model to be true are assumed. Concavity of this
criterion is proved. Thus, the results of optimal design theory apply in this context and optimal designs can be
constructed and checked by the General Equivalence Theorem.
Some illustrative examples are provided.
SUBJECT AREA:
C9
SUGGESTED CITATION:
Chiara Tommasi and Jesus Lopez Fidalgo,
"Bayesian optimal designs for discriminating between non-Normal models"
(May 2007).
UNIMI - Research Papers in Economics, Business, and Statistics.
Statistics and Mathematics.
Working Paper 26.
http://services.bepress.com/unimi/statistics/art26
Paper presented by S.M. Iacus.
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