Some Results on the Control of the False Discovery Rate under Dependence

By Alessio Farcomeni. 2007

92 citations recorded by the source corpus.

Subfields: Hardware and Architecture, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/bd53ebf7e28d0ee4d6904be1875c5a931d5b28bf

How researchers see this paper

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

In Hardware and Architecture

In Management Science and Operations Research

In Statistics and Probability

Citations in the corpus

Two simple sufficient conditions for FDR control (2008, result): “The results we know of for this case are found in works of Sarkar (2008) and Farcomeni (2007).”
Adaptive FDR control under independence and dependence (2007, methodology): “In this sense our results are novel with respect to those of Farcomeni (2007).”
Global Testing and Large-Scale Multiple Testing for High-Dimensional Covariance Structures (2017, background): “The effects of dependency on multiple testing procedures have been considered, for example, in Benjamini and Yekutieli (2001); Storey et al. (2004); Qiu et al. (2005); Farcomeni (2007); Wu (2008); Efron (2007); Sun and Cai (2009); Sun et al. (2015).”
Multiple hypothesis testing in experimental economics (2019, unknown): “4 in Romano and Wolf (2010) now follows simply by nothing that the Glivenko-Cantelli theorem, strong law of large numbers and continuous mapping theorem ensure that P̂n satisfies (a) – (d) with probability one under P .”
Journal of the American Statistical Association Estimating False Discovery Proportion under Arbitrary Covariance Dependence Estimating False Discovery Proportion under Arbitrary Covariance Dependence (unknown): “We need the following definition for weakly dependent normal random variables; other definitions can be found in Farcomeni (2007) are called weakly dependent normal variables , if lim p→∞ p −2 i,j |a ij | = 0, (6) where a ij denote the (i, j )th element of the covariance matrix A.”
Optimal weighting for false discovery rate control (2008, background): “Later, this procedure was proved to control the FDR under positive dependencies (PRDS, see [4]) and some other works show that this should also be the case under “realistic” general dependencies (see e.g. [ 10 ] and [18]).”
Large‐scale multiple testing under dependence (2009, methodology): “However, the works by Benjamini and Yekutieli (2001), Farcomeni (2007) and Wu (2008) show that FDR is controlled at the nominal level by the Benjamini and Hochberg (1995) step-up procedure and the adaptive p-value procedure (Benjamini and Hochberg, 2000; Genovese and Wasserman, 2004) under…”