The quality of statistical inference essentially depends on how complex we assume the underlying statistical model to be: generally, the richer the model, the worse the quality of statistical inferences. On the other hand, if the proposed model is too simple, it may not be able to provide a reasonable fit to the data. In an adaptive setup, instead of one particular model one deals with a family of models, often ordered or embedded from simple to complex. Depending on the statistical problem at hand (for instance, regression function estimation, testing hypothesis, confidence set), the general problem of adaptation is, loosely formulated, to design a so called adaptive method for solving this statistical problem which performs in multiple model situation as good as in a single model, or, if this is not possible, with the smallest loss of quality.
Sunday, October 9, 2011 - 6:00pm
Eurandom, Eindhoven, The Netherlands