Advanced Statistical Methods
This course introduces the main 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.
Instructor assistant: Nikita Puchkin
Zoom: https://us02web.zoom.us/j/81096629968?pwd=NG13S1IvZ3NKekI5UVRNaTNqZ0pPdz09
Schedule:
Monday, 16:00 - 19:00
Thursday, 16:00 - 19:00
All lecture materials can be found in the script.
If you have any questions about the course, please, write to npuchkin@gmail.com.
Useful links: hand-written lecture notes
Project list: link
Project assessment criteria: link
Questions for the exam: link
Homework 1 (deadline: February 21, Monday, 16:00)
Complete exercises 1.2.3, 1.3.1, 1.4.5, 1.6.2, 1.6.3, and 1.6.8 from the script.
Remark: in Ex. 1.3.1, do not forget to specify the decomposition from (vi).
Homework 2 (deadline: March 3, Thursday, 16:00)
Complete exercises 3.2.2, 3.3.2, 3.6.1, 4.1.3, 4.2.1, and 4.6.1 from the script.
Homework 3 (deadline: March 10, Thursday, 16:00)
Complete exercises 5.4.1, 5.4.2, 5.4.4, 5.5.2, 6.3.1, and 6.3.6 from the script.
Homework 4 (deadline: March 17, Thursday, 16:00)
Complete exercises 9.1.2, 9.1.3, 9.2.1, 10.2.3, 10.2.4, and 10.2.8 from the script.
Skoltech students must upload their solutions to Canvas. Those students, who do not have an access to Canvas, can send a PDF with solutions to npuchkin@gmail.com.
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