(by Garvesh Raskutti)
I enjoyed today much more than yesterday since I am slowly figuring out how to navigate the mass of talks. I found that when I am unsure what to go to, the invited sessions usually end up being a better choice. The talks are longer meaning the speakers have a chance to give proper background and explain the results in more detail. The 15 minute talks are mainly useful just to find out what people are working on.
I attended the discussion on Bayesian analysis hosted by Professor Gelman, Professor Berger and Professor Robert. They all presented interesting points of view on the difference between ‘Subjective Bayesians,’ ‘Objective Bayesians’ and ‘Frequentists.’ In particular I liked some of the points Professor Berger made about non-parametric Bayesian methods. The fact that the models are difficult to interpret I think is a genuine concern. Furthermore, the common belief that non-parametric methods make fewer assumptions is simply not true, a point made by Professor Berger. I also liked the fact that Professor Gelman highlighted the importance of the underlying research question and conclusions drawn from the methods being more critical than the choice of methods. In general, I feel it is difficult to discuss which methods are better or worse without discussing them with respect to a specific scientific goal.
I also attended the invited session on Sparse Regression and High-Dimensional Statistical Analysis. I especially liked the talk by Professor Rigollet on a new method that achieves optimal performance for prediction error under the sparse regression model. The presentation was extremely clear and transparent and completely solved the problem of optimal prediction error for the sparse linear models.