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...

Ful tanımlama

Kaydedildi:
Detaylı Bibliyografya
Yazar: Hermann, Philipp
Diğer Yazarlar: Holzmann, Hajo (Prof. Dr.) (Tez danışmanı)
Materyal Türü: Dissertation
Dil:İngilizce
Baskı/Yayın Bilgisi: Philipps-Universität Marburg 2021
Konular:
sparsity
variable selection
robust regression
Mathematik
high-dimensional regression
linear
sign-consistency
heteroscedasticity
adaptive LASSO
Huber loss
Mathematics
Online Erişim:PDF Tam Metin
Etiketler: Etiketle
Etiket eklenmemiş, İlk siz ekleyin!
No references were found for this record.

Benzer Materyaller