This master class will first provide an overview of the basic theory of optimality in semiparametric models, including for example the definition of optimality, the computation of information bounds, and efficient influence functions. The class will then discuss applications of the general principles to concrete statistical models. The final part of the class will focus on very recent results on optimal estimation and inference in high-dimensional models.
This class is free of charge but places are limited.
The programme for this masterclass can be downloaded here