Mean and Location Estimation with Optimal Constants - UW Madison

Explore a comprehensive lecture on mean and location estimation in statistics, delivered by Jasper Lee from UW Madison. Delve into the fundamental challenges of accurately estimating distribution means from i.i.d. samples and determining location when the distribution shape is known. Examine the limitations of established asymptotic theories, including sample mean and maximum likelihood estimation, particularly in scenarios requiring high confidence with limited samples. Discover recent advancements in finite sample and high probability theories that yield optimal constants. Learn about new mean estimators for 1-dimensional and high-dimensional regimes with tight error bounds, as well as novel characterizations of location estimation that surpass traditional MLE approaches. Gain insights into future research directions in this field, based on collaborative work with Paul Valiant, Shivam Gupta, and Eric Price.