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...

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Bibliographische Detailangaben
1. Verfasser: Hermann, Philipp
Beteiligte: Holzmann, Hajo (Prof. Dr.) (BetreuerIn (Doktorarbeit))
Format: Dissertation
Sprache:Englisch
Veröffentlicht: Philipps-Universität Marburg 2021
Schlagworte:
sparsity
variable selection
robust regression
Mathematik
high-dimensional regression
linear
sign-consistency
heteroscedasticity
adaptive LASSO
Huber loss
Mathematics
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