Alternatives to the Chi-Square Test for Evaluating Rank Histograms from Ensemble Forecasts
By Kim Elmore. 2005
50 citations recorded by the source corpus.
Subfields: Atmospheric Science, Management Science and Operations Research, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/4f7120d06137768df041e440464cf6be911bbf92
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Atmospheric Science
- Ian T. Jolliffe
- Cristina Primo
- D. Wilks
- Tilmann Gneiting
- Achim Zeileis
- Jakob W. Messner
- Georg J. Mayr
- Thordis Linda Thorarinsdottir
In Management Science and Operations Research
In Statistics and Probability
- D. Wilks
- Ian T. Jolliffe
- Cristina Primo
- Khaled H. Hamed
- Sebastian Lerch
- J. Annan
- J. Hargreaves
- David B. Stephenson
Citations in the corpus
- Evaluating Rank Histograms Using Decompositions of the Chi-Square Test Statistic (2008, background): “It is common to use rank histograms to evaluate the performance of ensemble forecasting systems (see Elmore 2005, hereinafter E05, and references cited therein).”
- Sampling distributions of the Brier score and Brier skill score under serial dependence (2010, methodology): “Bradley et al. (2008) derive the variance of the sampling distribution of the BS, assuming that the (yi, xi) pairs are independent random samples from the (single, homogeneous) joint distribution of the forecasts and observations (Murphy and Winkler, 1987).”