Отправить статью по E-mail Variance Reduction in Monte Carlo Estimators via Empirical Variance Minimization
- Авторы: Belomestny D.V.1,2, Iosipoi L.S.1,3, Zhivotovskiy N.K.1,2,4
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Учреждения:
- National Research University Higher School of Economics
- University of Duisburg-Essen
- Faculty of Mechanics and Mathematics
- Institute for Information Transmission Problems
- Выпуск: Том 98, № 2 (2018)
- Страницы: 494-497
- Раздел: Mathematics
- URL: https://journals.rcsi.science/1064-5624/article/view/225565
- DOI: https://doi.org/10.1134/S1064562418060261
- ID: 225565
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Аннотация
For Monte Carlo estimators, a variance reduction method based on empirical variance minimization in the class of functions with zero mean (control functions) is described. An upper bound for the efficiency of the method is obtained in terms of the properties of the functional class.
Об авторах
D. Belomestny
National Research University Higher School of Economics; University of Duisburg-Essen
Email: iosipoileonid@gmail.com
Россия, Moscow; Duisburg and Essen
L. Iosipoi
National Research University Higher School of Economics; Faculty of Mechanics and Mathematics
Автор, ответственный за переписку.
Email: iosipoileonid@gmail.com
Россия, Moscow; Moscow
N. Zhivotovskiy
National Research University Higher School of Economics; University of Duisburg-Essen; Institute for Information Transmission Problems
Email: iosipoileonid@gmail.com
Россия, Moscow; Duisburg and Essen; Moscow
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