Advanced course in high-dimensional statistics

Relevant statistical methods for data with high dimensions

Duration

10 to 20 h

Syllabus

Regression models with big data: motivation for variable selection 
(e.g. text regression, treatment effects with many controls, Bayesian 
networks and high-d time series), regularization, sparsity and a new 
theory for Statistics

Variable selection: best subset, Bayesian variable selection and 
computational challenges

Penalized likelihood methods: lasso and variants

Post selection inference: an overview of valid statistical methods 
for estimation and testing with big data

Network models and high-d covariance estimation

Prerequisites

“Foundations of Data Science” and Part I of “Introduction to Causal inference“

Credential

Taught as part of Summer School Week II, PhD courses, Statistical Modelling and Inference MSc in Data Science. Executive (ad-hoc) courses