Mini – Course: High-dimensional Statistics
Time:
Venue/Location: Trường Đại học Khoa học Tự nhiên - ĐHQG TP. Hồ Chí Minh 227 Nguyễn Văn Cừ, Quận 5, TP. Hồ Chí Minh
Objective: Classical statistical methods developed during the last century were suitable when the number of observations is much larger than the number of parameters to infer. Unfortunately, many fields such as astronomy, genetics, medicine or neuroscience produce large and complex data sets, and consequently with models containing a large number of parameters for which classical tools are not adapted. This issue is often referred as the curse of dimensionality. The goal of this course is to provide most of fundamental statistical tools to face with high-dimensional data. The aim is to present the main concepts and ideas on some selected topics of high-dimensional statistics based on modern nonparametric methodologies such as multiple testing, kernel, wavelets and penalized estimators with a special focus on Lasso estimation. Theoretical aspects are motivated by applicable developments of presented methods.
Expected participants:
- Undergraduate students (year 3, 4), Master students and PhD. candidates in
- Mathematics, Statistics, Data Science or Information Technology.
- Lecturers and researchers who are interested in this field.
Co-host Institution and Sponsors:
- Vietnam Institute for Advanced Study in Mathematics (VIASM)
- University of Science, VNU – HCM
Lecturer: Vincent Rivoirard, University Paris Dauphine, France
Language: English
Registration
- Registration link: Here
- Deadline for Registration: July 14, 2024
- Deadline for financial support application & registration: July 07, 2024
(Limited funding is available for participants from outside HCM City)
Contact:
Hoàng Văn Hà, email: hvha@hcmus.edu.vn
Nguyen Hong Anh, email: nguyenhonganh@viasm.edu.vn