High-dimensional, robust, heteroscedastic variable selection with the adaptive LASSO, and applications to random coefficient regression
In this thesis, theoretical results for the adaptive LASSO in high-dimensional, sparse linear regression models with potentially heavy-tailed and heteroscedastic errors are developed. In doing so, the empirical pseudo Huber loss is considered as loss function and the main focus is sign-consistency o...
Salvato in:
Autore principale: | |
---|---|
Altri autori: | |
Natura: | Dissertation |
Lingua: | inglese |
Pubblicazione: |
Philipps-Universität Marburg
2021
|
Soggetti: | |
Accesso online: | PDF Full Text |
Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
No references were found for this record.