18 citations · 18 across the 3 of their papers we have counts for
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Efficient Multiple Testing Adjustment for Hierarchical Inference
Claude Renaux, Peter Bühlmann
Hierarchical inference in (generalized) regression problems is powerful for finding significant groups or even single covariates, especially in high-dimensional settings where iden…
Multicarving for high-dimensional post-selection inference
Christoph Schultheiss, Claude Renaux, Peter Bühlmann
We consider post-selection inference for high-dimensional (generalized) linear models. Data carving (Fithian et al., 2014) is a promising technique to perform this task. However, i…
Group Inference in High Dimensions with Applications to Hierarchical Testing
Zijian Guo, Claude Renaux, Peter Bühlmann +1
High-dimensional group inference is an essential part of statistical methods for analysing complex data sets, including hierarchical testing, tests of interaction, detection of het…