False Discovery Rates for Spatial Signals
By Yoav Benjamini, Ruth Heller. 2007
233 citations recorded by the source corpus.
Subfields: Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/f8342f184985f75b8f903ee45b2d4d87a9469789
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In Statistics and Probability
- Wolfgang Huber
- Nikolaos Ignatiadis
- Jinchi Lv
- E.J. Candes
- Yingying Fan
- Étienne Roquain
- Tommaso Cai
- Gilles Blanchard
Citations in the corpus
- Panning for Gold: Model-free Knockoffs for High-dimensional Controlled Variable Selection (2016, background): “A consequence of this mismatch is that the result of the analysis may be completely misleading, as beautifully argued in Pacifico et al. (2004); Benjamini and Heller (2007); Siegmund et al. (2011), see also Chouldechova (2014) and Brzyski et al. (2016) for later references.”
- Covariate powered cross‐weighted multiple testing (2017, background): “clusters. This also has the potential advantage of reducing high-frequency components of the noise. Moreover, in such setups, the cluster is the natural unit of discovery, not the individual feature (Benjamini and Heller, 2007; Siegmund et al., 2011). If avoiding dependence by modifying the analysis”
- Large‐scale multiple testing under dependence (2009, background): “The works by Yekutieli and Benjamini (1999), Genovese et al. (2006) and Benjamini and Heller (2007) showed that incorporating scientific , spatial information into a multiple-testing procedure may greatly improve the efficiency.”
- Two simple sufficient conditions for FDR control (2008, methodology): “…large impact on power (Genovese et al., 2006; Roquain and van de Wiel, 2008; see also above Remark 2.8), while using a volume Λ different from the cardinality measure can be of relevance for some application cases (see Benjamini and Hochberg, 1997; Benjamini and Heller, 2007; and Remark 2.3 above).”
- Cluster-extent based thresholding in fMRI analyses: Pitfalls and recommendations (2014, methodology): “…based thresholding methods we discuss extensively here, several recent alternatives have been proposed, including the threshold-free cluster enhancement (TFCE)method (Smith and Nichols, 2009) and hierarchical false discovery rate (FDR) control on clusters (Benjamini and Heller, 2007).”
- Adaptive FDR control under independence and dependence (2007, result): “This allows in particular to recover results very similar to those of Benjamini and Heller (2007) and can also be used to prove that a (generalized) Storey estimator can be used to control the weighted FDR. The modifications needed to include this generalizations are relatively minor; we omit the de”
- Topological complexity of smooth random functions (2011, background): “We shall not describe this approach here, but rather point you to [12, 66] which explain and support FDR.”
- Discovering the false discovery rate (2010, background): “The weights can be based on outside information, or on information from initial testing or on the size of the set (Benjamini and Heller, 2007).”
- Optimal weighting for false discovery rate control (2008, background): “The problem of finding procedures that control the above quantities has been widely studied in the multiple testing literature (see e.g. [1, 4, 23, 14, 20, 3 ]).”
- Topological FDR for neuroimaging (2010, unknown): “Numerous authors have remarked that the fundamental effects of interest in fMRI are distributed and spatially continuous (e.g. Heller et al 2006, see also Pacifico et al 2004 and Benjamini and Heller 2007).”
- False discovery rate revisited: FDR and topological inference using Gaussian random fields (2009, methodology): “…the fundamental units of interest are the spatially contiguous clusters of voxels that are activated together, we set out to approximate these cluster units from the data by a clustering algorithm especially tailored for fMRI data ” (see also Paci fi co et al., 2004; Benjamini and Heller, 2007).”
- Panning for gold: ‘model‐X’ knockoffs for high dimensional controlled variable selection (2016, background): “A consequence of this mismatch is that the result of the analysis may be completely misleading, as beautifully argued in Pacifico et al. (2004); Benjamini and Heller (2007); Siegmund et al. (2011), see also Chouldechova (2014) and Brzyski et al. (2016) for later references.”