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

Hermann, Philipp

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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書誌詳細
第一著者:	Hermann, Philipp
その他の著者:	Holzmann, Hajo (Prof. Dr.) (論文の指導者)
フォーマット:	Dissertation
言語:	英語
出版事項:	Philipps-Universität Marburg 2021
主題:	sparsity variable selection robust regression Mathematik high-dimensional regression linear sign-consistency heteroscedasticity adaptive LASSO Huber loss Mathematics
オンライン･アクセス:	PDFフルテキスト
タグ:	タグ追加タグなし, このレコードへの初めてのタグを付けませんか!

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

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