= Sara van de Geer
{c}
<Sara van de Geer> develops the mathematical theory of <high-dimensional statistics>. Her <Statistical Theory for High-Dimensional Models by Sara van de Geer> studies prediction bounds for the <Lasso> and <Square-root Lasso>, and inference after correcting shrinkage bias.<Statistical Theory for High-Dimensional Models by Sara van de Geer>[Abstract and chapters 1–2]{ref}
The distinction is useful: an estimator that predicts well may still bias an individual coefficient toward zero. A de-sparsified estimator adds a correction whose leading error can have an approximately <normal distribution>. Bounds on the remaining error, together with sparsity and design assumptions, then justify <confidence intervals>. The same notes connect <generic chaining> and <concentration inequalities> with control of random-process suprema.<Statistical Theory for High-Dimensional Models by Sara van de Geer>[Section 2.4, Theorems 2.4.1–2.4.2, and chapter 3]{ref}
Back to article page
