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October 14, 2026
At HSE University–St Petersburg, Experts Discuss How AI Changes Tourist Routes and Cultural Heritage
Neural networks are no longer simple travel assistants; now they decide what to include on the route and what to skip. Artificial intelligence changes how people perceive a place and shapes the image of tourist centres. At the II International Scientific and Practical Conference ’Information Technologies in the Preservation and Promotion of Cultural Heritage,’ researchers discussed who controls algorithmic solutions and in what ways, and whether authentic traditions get lost in them.
October 12, 2026
Road Injuries Leading Cause of Death Among Men Aged 10 to 39
Worldwide, road injuries are the leading cause of death among males aged 10–39. An international team of researchers reached this conclusion as part of the Global Burden of Disease Study 2023, analysing data from 204 countries between 1990 and 2023. Their findings have been published in The Lancet Public Health, with HSE University Professor Vasily Vlassov listed among the collaborators.
October 8, 2026
HSE Experts Take Part in 23rd Annual Meeting of Valdai Discussion Club
The 23rd Annual Meeting of the Valdai Discussion Club was held from September 28 to October 1, 2026 under the theme ‘Responsibility for the Future: Limits of the Possible, or Limitless Possibilities?’ The forum brought together 120 experts from 40 countries, including representatives of China, the United States, India, Brazil, the United Kingdom, Germany, Egypt, Iran, and Japan.

 

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Простое доказательство робастности метода наименьших квадратов с урезанием для линейной регрессионной модели

Проблемы управления. 2016. № 5. С. 10–13.
Shvedov A. S.
Language: Russian
Full text
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Keywords: робастная регрессияметод наименьших квадратов с урезаниемпороговая точка
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Experimental and Analytic Comparison of the Accuracy of Different Estimates of Parameters in a Linear Regression Model
Goryainova E. R., Botvinkin E. A., Automation and Remote Control 2017 Vol. 78 No. 10 P. 1819–1836
We consider LS-, LAD-, R-, M-, S-, LMS-, LTS-, MM-, and HBR-estimates for the parameters of a linear regression model with unknown noise distribution. With computer modeling for medium sized samples, we compare the accuracy of the considered estimates for the most popular probability distributions of noise in a regression model. For different noise distributions, ...
Added: December 29, 2017
Робастная регрессия с применением T-распределения и EM-алгоритма
Shvedov A. S., Экономический журнал Высшей школы экономики 2011 Т. 15 № 1 С. 68–87
The paper deals with a linear regression model. The EM algorithm is popular tool for maximum likelihood estimation of the parameters of regression model. It provides a method of robust regression under the assumption that the disturbances are independent and have identical multivariate t distribution. Previous work focused on the method of maximum likelihood estimation ...
Added: November 29, 2012
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