An Overview of Classical Robust Statistics and Generalizations to the Future

Explore key concepts in classical robust statistics and their modern applications in this lecture by Po-Ling Loh from the University of Cambridge. Gain insights into reliable estimation and inference methods for data with contaminated distributions. Discover how traditional robust statistics principles are being adapted to address contemporary challenges, including heterogeneous distributions, novel contamination forms, and private hypothesis testing. Delve into the evolving landscape of robust statistical methods and their relevance in today's data-driven world.