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  • Метод обнаружения структурного сдвига в модели авторегрессионной условной гетероскедастичности: случай распределения Стьюдента
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News
June 5, 2026
Neural Network Maps as a Method for Constructing Mathematical Models
Scientists from HSE University–Nizhny Novgorod and the Institute of Physics Belgrade, Serbia, are jointly exploring the application of machine learning techniques and neural networks to the study of nonlinear dynamics. Natalya Stankevich, Leading Research Fellow at the Laboratory of Topological Methods in Dynamics of the Faculty of Informatics, Mathematics, and Computer Science at HSE University–Nizhny Novgorod, spoke to the HSE News Service about this international project.
June 5, 2026
‘In the Age of Technology, It Is Interesting to Look into the Past and Think about What We Can Take from It
Polina Tabakova decided to apply for a Philology degree at HSE in Nizhny Novgorod because she grew up in Mari El and did not want to move far away from the Russian forests. In an interview for the Young Scientists of HSE University project, she spoke about the genre of the campus novel, the existential drama of Kolobok, and a blackout version of Eugene Onegin.
June 5, 2026
HSE Scientists Develop Method to Compress Large Language Models Without Losing Quality
Researchers from the AI and Digital Science Institute at the HSE Faculty of Computer Science have developed a new compression method for large language models such as GPT and LLaMA that reduces their size by 25–36% without additional training or significant loss of accuracy. This is the first approach to use mathematical transformations—specifically, rotations of model weights—to make models more amenable to compression with structured matrices. The study results have been published in ACL Findings 2025. The code is available on GitHub.

 

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Метод обнаружения структурного сдвига в модели авторегрессионной условной гетероскедастичности: случай распределения Стьюдента

Математическое моделирование. 2023. Т. 35. № 1. С. 51–58.
Borzykh D., Yazykov A.

We consider two methods of structural break detection in a piecewise generalized model of autoregressive conditional heteroscedasticity. The first method is based on Kolmogorov–Smirnov statistics and is called KS-method. The second one is based on the cumulative sums and is called KL-method. In this paper, we compare the KS- and KL-methods under the assumption of Student conditional distribution of random errors. The results of our Monte Carlo experiments  were as follows: the KL-method lost to the KS-method both in terms of the average probability of first  type error and in terms of the average power structural break detection.

Research target: Mathematics Computer Science Economics and Management
Language: Russian
Full text
DOI
Keywords: GARCH-моделиструктурные сдвигиstructural breakCUSUMCUSUMStudent's t-distributionGARCH modelGARCH-tGARCH-tt-распределение Стьюдента
Publication based on the results of:
Nonstationary random environments and their applications (2023)
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