Theory and Methods of Statistical Learning and Inference
MATH 343
Estimators and properties (efficiency, consistency, sufficiency); loss functions. Fisher information, asymptotic properties and distributions of estimators. Exponential families. Point and interval estimation, delta method. Neyman-Pearson lemma; likelihood ratio tests; multiple testing; design and the analysis of variance (ANOVA). High-dimensional data; statistical regularization and sparsity; penalty and prior formulations; model selection. Resampling methods; principal component analysis, mixture models. Prerequisite: (Statistical Science 240L, 230, or 231) and (Mathematics 202, 212, 219, or 222). Not open to students with credit for STA 250. Recommended prerequisite: Statistical Science 210, 360, and (Mathematics 221, 218, or 216). Instructor: Staff
Prerequisites
Prerequisite: (Statistical Science 240L, 230, or 231) and (Mathematics 202, 212, 219, or 222). Not open to students with credit for STA 250.