Independent University of Moscow, 2017
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.
The lectures and seminars will be held in IUM (Independent University of Moscow).
Dates and place:
February 7 (Tuesday), Lecture, 17:30 – 20:30, IUM, room 401;
February 8 (Wednesday), Seminar, 17:30 – 20:30, IUM, room 310;
February 14 (Tuesday), Lecture, 17:30 – 20:30, IUM, room 401;
February 15 (Wednesday), Seminar, 17:30 – 20:30, IUM, room 310;
February 21 (Tuesday), Lecture, 17:30 – 20:30, IUM, room 401;
February 22 (Wednesday), Seminar, 17:30 – 20:30, IUM, room 310.
Lecturer: Vladimir Spokoiny.
Course assistants: Nikita Zhivotovskiy, Leonid Iosipoi, Alexey Naumov, Maxim Panov.
For general questions please contact email@example.com.
 Vladimir Spokoiny. Nonparametric estimation: parametric view;
 Larry Wasserman. All of Nonparametric Statistics. Springer Texts in Statistics, 2006.
 Gerda Claeskens, Nils Lid Hjort. Model Selection and Model Averaging. Cambridge Series in Statistical and Probabilistic Mathematics, 2008.