Controlling the Proportion of Falsely Rejected Hypotheses when Conducting Multiple Tests with Climatological Data
By Valérie Ventura, Christopher J. Paciorek, J. Risbey. 2004
221 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/c92b8d94bfa68bc246eda16ecc5d0dc924f1fbe7
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- D. Wilks
- Daniel Cooley
- Ian T. Jolliffe
- Erin M. Schliep
- Stephan R. Sain
- Jennifer A. Hoeting
- Sebastian Lerch
- Stefano Soatto
In Statistics and Probability
- D. Wilks
- Ian T. Jolliffe
- Daniel Cooley
- Erin M. Schliep
- Stephan R. Sain
- Jennifer A. Hoeting
- Khaled H. Hamed
- Sebastian Lerch
Citations in the corpus
- Uncertainty and Inference for Verification Measures (2007, methodology): “Finally, Ventura et al. (2004) note that most procedures are conservative, in that the quoted controlled level of the FDR is an upper bound, and it may not be a very strong bound.”
- “The Stippling Shows Statistically Significant Grid Points”: How Research Results are Routinely Overstated and Overinterpreted, and What to Do about It (2016, methodology): “Controlling the FDR (Benjamini and Hochberg 1995, Ventura et al. 2004, Wilks 2006) has many favorable attributes, including only modest sensitivity to spatial autocorrelation in the underlying data.”
- A comparison study of extreme precipitation from six different regional climate models via spatial hierarchical modeling (2010, background): “These issues are non-trivial, and we do not attempt to address them here, for reference please see Ventura et al. (2004).”