4 papers
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…
Hierarchical inference for genome-wide association studies: a view on methodology with software
Claude Renaux, Laura Buzdugan, Markus Kalisch +1
We provide a view on high-dimensional statistical inference for genome-wide association studies (GWAS). It is in part a review but covers also new developments for meta analysis wi…