Events

This talk will consider the assessment of semiparametric and other highly-parametrised models from the perspective of foundational principles of parametric statistical inference. It is based on a generalization of information separation using classical ideas of sufficiency and ancillarity. For semi-parametric models this leads to an evaluation of models by a type of within-sample prediction error. The theory is illustrated through several examples. This is joint work with Heather Battey.