On “Field Significance” and the False Discovery Rate

By D. Wilks. 2006

488 citations recorded by the source corpus.

Subfields: Environmental Engineering, Global and Planetary Change, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/995d8a1e9d28e4388620fbad2635bd063381d413

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In Environmental Engineering

In Global and Planetary Change

In Statistics and Probability

Citations in the corpus

Statistical significance of climate sensitivity predictors obtained by data mining (2014, methodology): “Several methods have been proposed for establishing the significance of relationships in portions of spatially and temporally varying arrays [von Storch, 1982; Livezey and Chen, 1983; Wilks, 2006; DelSole and Yang, 2011].”
The distribution of Kendall's tau for testing the significance of cross-correlation in persistent data (2011, methodology): “Wilks (2006) suggests two simpler procedures to test field significance that are more robust than the counting approach in the presence of spatial correlation.”
The impact of the MJO on clusters of wintertime circulation anomalies over the North American region (2012, methodology): “The approach is summarized briefly here, and the reader is referred to Benjamini and Hochberg (1995) and Wilks (2006) for further details.”
Uncertainty and Inference for Verification Measures (2007, background): “Ventura et al. (2004) describe a modification, due to Genovese and Wasserman (2004), that improves power, while still controlling the FDR. Wilks (2006b) advocates the use of the FDR in an atmospheric science context.”
Multisegment statistical bias correction of daily GCM precipitation output (2013, background): “The gamma distribution is among the preferred distributions [Hanson and Vogel, 2008] to fit daily precipitation data mainly because of its skewness to the right that suits the daily precipitation distribution and the lower limit of zero which constrains rainfall values [Wilks, 2006].”
“The Stippling Shows Statistically Significant Grid Points”: How Research Results are Routinely Overstated and Overinterpreted, and What to Do about It (2016, methodology): “Furthermore the size of that global hypothesis test (i.e., the probability of rejecting a global null hypothesis if it is true), is αglobal = αFDR (Wilks 2006).”