Theme: High-Dimensional Statistics
Course Title: Model selection and inference for high-dimensional data

Lecturer: Dr Davide Ferrari

Course Content
Modern data sets are increasingly large and complex due to the rapid development of data acquisition and storage capabilities. This course focuses on developing a rigorous understanding of modern statistical learning methods needed to model large data sets, assess the reliability of the selected models, and obtain accurate predictions. This course covers recent methodological developments in this area such as inference for high- dimensional regression, model selection and model combining methods, and post-selection inference methods.

Background Reading

  • Giraud, Christophe. Introduction to high-dimensional statistics. Vol. 138. CRC Press, 2014
  • Efron, Bradley. Large-scale inference: empirical Bayes methods for estimation, testing, and prediction. Vol. 1. Cambridge University Press, 2012