Stat. Theory for Big Data

Catalog description

Statistical Theory for Big Data: The course gives a thorough introduction to large sample theory of estimation and inference for Big Data analysis. The topics include: modes of convergence, central limit theorems for averages and quantiles, and asymptotic relative efficiency; estimating equations including the law of large numbers for random functions, consistency and asymptotic normality for maximum likelihood and M-estimators, the EM algorithm, and asymptotic confidence regions and hypotheses tests; models of non-identically distributed or dependent random variables, etc.