False Discovery Control for Multiple Tests of Association Under General Dependence

By Nicolai Meinshausen. 2006

71 citations recorded by the source corpus.

Subfields: Food Science, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/1973f94f357cbffa8e845c7e13e630382c1d83e7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Food Science

1. Jelle J. Goeman
2. Aldo Solari
3. Jesse Hemerik
4. Peter Buhlmann
5. Nicolai Meinshausen
6. Marloes H. Maathuis

In Management Science and Operations Research

1. Aldo Solari
2. Jelle J. Goeman
3. Jesse Hemerik
4. Peter Buhlmann
5. Marloes H. Maathuis
6. Nicolai Meinshausen
7. Jiashun Jin
8. Guang Cheng

In Statistics and Probability

1. Jelle J. Goeman
2. Aldo Solari
3. Peter Buhlmann
4. Jesse Hemerik
5. Nicolai Meinshausen
6. Marloes H. Maathuis
7. Sara A. van de Geer
8. Cun-Hui Zhang

Citations in the corpus

• Only closed testing procedures are admissible for controlling false discovery proportions (2019, methodology): “…to higher criticism (Donoho et al., 2004), to the local tests implied by the Dvoretzky-Kiefer-Wolfowitz inequality (Genovese and Wasserman, 2004; Meinshausen, 2006), to the local tests implied by second and higher order generalized Simes constants (Cai and Sarkar, 2008; Gou and Tamhane, 2014),…”
• Permutation-based simultaneous confidence bounds for the false discovery proportion (2018, methodology): “See also Section 3.2 in Meinshausen (2006).”
• Asymptotic optimality of the Westfall--Young permutation procedure for multiple testing under dependence (2011, methodology): “1N. Meinshausen and M. H. Maathuis contributed equally to this work.”
• Hierarchical testing of variable importance (2008, background): “This additional source of variation is, however, easily incorporated in a simulation-based approach, as for example in Meinshausen (2006).”
• Multiple hypothesis testing in genomics (2014, methodology): “We can obtain permutation-based confidence intervals for FDP of the form (12) that are analogous to those of Goeman and Solari using a method due to Meinshausen [83].”
• Multiple Testing for Exploratory Research (2011, methodology): “e so far only attracted theoretical interest as in these methods value of k may not be chosen post hoc, and nobody knows how to choose k a priori in an applied setting. A recent permutation method of Meinshausen (2006) can be seen as a method that controls k-FWER simultaneously for all values of k, ”
• Journal of the American Statistical Association Estimating False Discovery Proportion under Arbitrary Covariance Dependence Estimating False Discovery Proportion under Arbitrary Covariance Dependence (unknown): “See Genovese and Wasserman (2004), Meinshausen (2006), Efron (2007), Friguet, Kloareg, and Causeur (2009), etc.”