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David Lamparter: Stability Selection for Error Control in High-Dimensional Regression
Adviser: Prof. Dr. Peter Bühlmann
August 2011
Abstract:
In the recent past, the development of statistical methods for high-dimensional problems has greatly advanced leading to methods for model selection such as the lasso. However, the question of error control in high-dimensional settings has proven to be difficult. Recently, an approach called stability selection has been proposed to tackle the problem. It combines a method for model selection and subsampling to deliver a form of error control. In this thesis, some variants of stability selection are introduced. It was tested if error control would actually hold up. Furhermore, some conditions were isolated where using these variants might have beneficial effects.
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