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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