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...
Uloženo v:
Hlavní autor: | |
---|---|
Další autoři: | |
Médium: | Dissertation |
Jazyk: | angličtina |
Vydáno: |
Philipps-Universität Marburg
2021
|
Témata: | |
On-line přístup: | Plný text ve formátu PDF |
Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
No references were found for this record.