False Discovery Control for Random Fields

By M. Pacifico, Christopher R. Genovese, Isabella Verdinelli, Larry A. Wasserman. 2004

175 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/a099e74bcfc047a1cff5a0e18b9585aaed0ce7ff

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Citations in the corpus

Two simple sufficient conditions for FDR control (2008, methodology): “2004 Such a setting considered for example by Perone Paciﬁco et al. ( ).”
Uncertainty and Inference for Verification Measures (2007, background): “Other recent references related to the topic include Black (2004), Cox and Wong (2004), Efron (2004), Perone Pacifico et al. (2004), Storey (2002), and Storey et al. (2004) Another aspect of hypothesis testing that has not been much discussed in the atmospheric science literature [Ventura et al. (20”
Balanced Control of Generalized Error Rates (2010, background): “These ideas are extended in [21], where in the context of random fields, the number of null hypotheses is uncountable.”
On false discovery control under dependence (2008, methodology): “[15] applied FDR-thresholding to construct conservative confidence envelopes for Gaussian random fields.”
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.”
Generalizations of the familywise error rate (2005, background): “These ideas are extended in [9], where in the context of random fields the number of null hypotheses is uncountable.”
Hierarchical testing of variable importance (2008, unknown): “While Pacifico et al. (2004) show FDR-control for random fields, a more closely related work is the technical report of Benjamini and Yekutieli (2003), where FDR-control for hierarchical tests is proposed.”
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.”
False Discovery Rates for Spatial Signals (2007, methodology): “FDRλ has been suggested by Pacifico et al. (2004) for a threshold procedure with data-dependent threshold T (X): A(T ) = {s ∈ D :X(s) ≥ T (X)}.”
False discovery rate revisited: FDR and topological inference using Gaussian random fields (2009, methodology): “The cluster-based FDR procedure described in Pacifico et al. (2004) defines a cluster as true if the proportion of its volume that is trulyactivated exceeds some threshold τ. This is an ad hoc definition and again rests on the assumption that some measure (i.e., volume) can be labelled as activate”