Stat 210b: Theoretical Statistics
UC Berkeley
Offerings
Overview
Introduction to modern theory of statistics; empirical processes, influence functions, M-estimation, U and V statistics and associated stochastic decompositions; non-parametric function estimation and associated minimax theory; semiparametric models; Monte Carlo methods and bootstrap methods; distributionfree and equivariant procedures; topics in machine learning. Topics covered may vary with instructor.
Logistics
Three hours of lecture per week.
Prerequisites
Statistics 210A and a graduate level probability course; a good understanding of various notions of stochastic convergence.