The new term is almost here. This year I am (once again) teaching Intermediate Statistical Theory which is a first year graduate course in mathematical statistics.
This course used to be populated mainly by students in the first year of the Statistics Ph.D. program (about 5-10 students). Last year I had over 100 students! Most were from Computer Science and related fields. The world has changed. Statistics is sexy.
In the olden days I followed Casella and Berger. I covered the traditional topics like: basic probability, convergence, sufficiency, estimation, testing, confidence intervals, large sample theory.
In response to changes on our field I have been gradually changing the topics. I threw out: unbiased estimation, most of sufficiency, ancillarity, completeness, the Rao-Blackwell theorem, most powerful tests, etc. I added: Hoeffding’s inequality, VC theory, minimax theory, nonparametric smoothing, the bootstrap, prediction and classification, model selection and causation.
The field of statistics is changing quickly. If we don’t change our courses accordingly we run the risk of becoming irrelevant. On the other hand, we don’t want to abandon teaching core statistical principles and just teaching the latest fads.
I am wondering what other people do. Have you changed your courses? Did I throw out too much? Are there other things I should include?