Adaptive robust methods for dependent big data models
In this paper we study high dimension statistical autoregressive models on the basis of the sequential analysis approach. To this end we use the model selection procedures developed in [4]. For such models we find conditions under which we show that these estimation procedures are efficient in the m...
| Published in: | Международная научная конференция "Робастная статистика и финансовая математика - 2020" (15-16 декабря 2020 г.) : сборник статей С. 4-13 |
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| Main Author: | |
| Other Authors: | , |
| Format: | Book Chapter |
| Language: | English |
| Subjects: | |
| Online Access: | http://vital.lib.tsu.ru/vital/access/manager/Repository/koha:000890954 Перейти в каталог НБ ТГУ |
| Summary: | In this paper we study high dimension statistical autoregressive models on the basis of the sequential analysis approach. To this end we use the model selection procedures developed in [4]. For such models we find conditions under which we show that these estimation procedures are efficient in the minimax sense. It should be emphasized that the efficiency property is shown without knowing either the regularity properties or the noise distribution in the models, i.e. in an adaptive and robust setting. |
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| Bibliography: | Библиогр.: 13 назв. |
| ISBN: | 9785946217507 9785946219907 |