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
Published: Philipps-Universität Marburg 2021
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Call Number: urn:nbn:de:hebis:04-z2021-02489
Publication Date: 2021-06-29
Date of Acceptance: 2021-04-19
Downloads: 31 (2024), 116 (2023), 129 (2022), 71 (2021)
License: https://creativecommons.org/licenses/by-nc-nd/4.0/
Access URL: https://archiv.ub.uni-marburg.de/diss/z2021/0248