Бакалавриат
2025/2026
Продвинутые статистические методы
Статус:
Курс по выбору (Прикладной анализ данных)
Где читается:
Факультет компьютерных наук
Когда читается:
3-й курс, 3, 4 модуль
Охват аудитории:
для своего кампуса
Преподаватели:
Слаболицкий Илья Сергеевич
Язык:
английский
Кредиты:
4
Контактные часы:
68
Course Syllabus
Abstract
Classical (mathematical) statistics usually turns out to be unsustainable and unrobust to real problems. Many data analysis tasks (in economics, finance and business) require alternative, more complex and advanced approaches, which will be applicable in practice. In this course, we will discuss a great amount of such topics. We will talk about their theoretical (mathematical and probabilistic) foundations and see how they can be applied to the practical problems.
Learning Objectives
- this course is focused on modern, complex and advanced data analysis methods, provides their overview, mathematical foundations and demonstrates their applications
Expected Learning Outcomes
- orms the ideas of semi-parametric and non-parametric statistics
- forms the ideas of copulae-based models.
- forms the ideas of online statistical learning.
- forms the ideas of elements of extreme value analysis.
Course Contents
- semi-parametric and non-parametric statistics
- copulae-based models
- online statistical learning
- elements of extreme value analysis
Assessment Elements
- home assignmentshome assignments during the course
- midterm 1midterm based on the 3 module
- midterm 2midterm based on the 4 module
- examfinal exam
Interim Assessment
- 2025/2026 4th module0.3 * exam + 0.2 * home assignments + 0.25 * midterm 1 + 0.25 * midterm 2
Bibliography
Recommended Core Bibliography
- All of nonparametric statistics : with 52 ill., Wasserman, L., 2006
- Wasserman, L. All of nonparametric statistics. – Springer Science & Business Media, 2006. – 270 pp.
Recommended Additional Bibliography
- All of statistics : a concise course in statistical inference, Wasserman, L., 2010