High-dimensional, robust, heteroscedastic variable selection with the adaptive LASSO, and applications to random coefficient regression

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...

Cijeli opis

Spremljeno u:
Bibliografski detalji
Glavni autor: Hermann, Philipp
Daljnji autori: Holzmann, Hajo (Prof. Dr.) (Savjetnik disertacije)
Format: Dissertation
Jezik:engleski
Izdano: Philipps-Universität Marburg 2021
Teme:
sparsity
variable selection
robust regression
Mathematik
high-dimensional regression
linear
sign-consistency
heteroscedasticity
adaptive LASSO
Huber loss
Mathematics
Online pristup:PDF cijeli tekst
Oznake: Dodaj oznaku
Bez oznaka, Budi prvi tko označuje ovaj zapis!
No references were found for this record.

Slični predmeti