• Corpus ID: 233476275

Efficient Multiple Testing Adjustment for Hierarchical Inference

  title={Efficient Multiple Testing Adjustment for Hierarchical Inference},
  author={Claude Renaux and Peter Buhlmann},
Hierarchical inference in (generalized) regression problems is powerful for finding significant groups or even single covariates, especially in high-dimensional settings where identifiability of the entire regression parameter vector may be ill-posed. The general method proceeds in a fully data-driven and adaptive way from large to small groups or singletons of covariates, depending on the signal strength and the correlation structure of the design matrix. We propose a novel hierarchical… 


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  • R. Meijer, J. Goeman
  • Mathematics, Medicine
    Biometrical journal. Biometrische Zeitschrift
  • 2015
This work presents a novel multiple testing method for testing null hypotheses that are structured in a directed acyclic graph (DAG) and can be seen as a generalization of Meinshausen's procedure for tree-structured hypotheses.