This course introduces some important notions, approaches, and methods of nonparametric statistics. The main topics include smoothing and regularization, model selection and parameter tuning, structural inference, efficiency and rate efficiency, local and sieve parametric approaches. The study is mainly limited to regression and density models. The topics of this course form an essential basis for working with complex data structures using modern statistical tools.

Prerequisites: Probability theory, linear algebra, mathematical analysis.

Руководитель семинара
Спокойный Владимир Григорьевич

Организации
Независимый Московский университет