We consider the problem of constructing optimal designs for model
discrimination between competing regression models. Various new properties of
optimal designs with respect to the popular T-optimality criterion are
derived, which in many circumstances allow an explicit determination of
T-optimal designs. It is also demonstrated, that in nested linear models the
number of support points of T-optimal designs is usually too small to
estimate all parameters in the extended model. In many cases T-optimal
designs are usually not unique, and in this situation we give a
characterization of all T-optimal designs. Finally, T-optimal designs are
compared with optimal discriminating designs with respect to alternative
criteria by means of a small simulation study.Comment: Published in at http://dx.doi.org/10.1214/08-AOS635 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
