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News
September 17, 2026
'I Wish That People Would Place Greater Trust in Science'
When Tatiana Eremicheva chose Fundamental and Computational Linguistics as her field of study, she thought it would be about learning languages. Instead, she discovered it was about helping people. In this interview for the HSE Young Scientists project, she discusses science as a way of understanding the world, billiards as a team-building activity, and why learning to read is not always as easy as it seems.
September 15, 2026
Immunity to Chaos: How Personal Resources Help Us Cope with the Challenges of a Turbulent World
International conflicts, crises and digital overload—the modern world puts our minds to the test every day. Traditional psychology often focuses on the consequences: anxiety, depression, and psychosomatic disorders. But what if we looked at the problem differently—through the lens of the resources that prevent us from breaking down? Psychological immunity is precisely this set of resources. Alena Zolotareva and her group, Psychological Immunity as a Resource for Positive Functioning, are developing an integrative model of this phenomenon, adapting diagnostic tools and preparing for large-scale empirical research. Why do psychologists need to collaborate with medical professionals, and how could their research transform preventive care in clinics and corporations?
September 11, 2026
How to Assess Students Knowledge in the Age of AI
A researcher at HSE University has proposed a flowchart to help lecturers decide how to assess students who use artificial intelligence. It shows where the use of AI should be restricted and where it can be incorporated into the learning process. The article has been published in IT Professional.

 

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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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