Advanced Topics in Statistical Inference

3 credits · Graduate credit

This course continues Stats 610, covering nonparametrics (nonparametric regression, splines, kernel methods, density estimation, risk, generalization bounds, overfitting); resampling and data splitting methods (cross-validation, stability selection, data splitting, parametric and nonparametric bootstrap), statistical problems in high dimensions (white noise model, classical nonparametrics, Stein's paradox, the Lasso and related algorithms and penalties. Additional topics will be selected by the instructor and may include post-selection inference, adaptive inference and sequential learning, empirical processes with applications to statistics, minimaxity, and Bayesian inference.