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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Bibliographic Details

Main Author:	Hermann, Philipp
Contributors:	Holzmann, Hajo (Prof. Dr.) (Thesis advisor)
Format:	Doctoral Thesis
Language:	English
Published:	Philipps-Universität Marburg 2021
Subjects:	robust regression sign-consistency Mathematik linear heteroscedasticity sparsity high-dimensional regression Huber loss variable selection adaptive LASSO Mathematics
Online Access:	PDF Full Text
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