High Dimensional Inference – a Literature Review [Articol]
| dc.contributor.author | Lopotenco, Alexandru | en |
| dc.contributor.author | Teleuca, Marcel | en |
| dc.date.accessioned | 2025-05-08T07:09:52Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | High dimensional inference has been a crucial topic in statistical learning, especially with the rise of data that has a lot of features yet is difficult to gather. We analyze regressions on data in large dimensions p that are much greater than the samples available, n. The usual method in constructing confidence intervals used for OLS is not useful when p » n since asymptotic normality fails. We survey literature that comes up with new method of constructing these intervals. | en |
| dc.identifier.citation | LOPOTENCO, Alexandru and Marcel TELEUCA. High Dimensional Inference – a Literature Review. In: International Conference dedicated to the 60th anniversary of the foundation of Vladimir Andrunachievici Institute of Mathematics and Computer Science, MSU, October 10-13 2024. Chisinau: [S. n.], 2024, pp. 93-99. ISBN 978-9975-68-515-3 | en |
| dc.identifier.isbn | 978-9975-68-515-3 | |
| dc.identifier.uri | https://msuir.usm.md/handle/123456789/18036 | |
| dc.language.iso | en | |
| dc.subject | statistical learning | en |
| dc.subject | high-dimensional inference | en |
| dc.subject | confidence intervals | en |
| dc.subject | lasso | en |
| dc.title | High Dimensional Inference – a Literature Review [Articol] | en |
| dc.type | Article |