Evaluating Rank Histograms Using Decompositions of the Chi-Square Test Statistic

By Ian T. Jolliffe, Cristina Primo. 2008

52 citations recorded by the source corpus.

Subfields: Applied Mathematics, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/bb6b6f91766e9e8fe49f3805957ff088e3ac7ef5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Applied Mathematics

1. J. Annan
2. J. Hargreaves
3. D. Wilks

In Management Science and Operations Research

1. J. Annan
2. J. Hargreaves
3. D. Wilks

In Statistics and Probability

1. J. Annan
2. J. Hargreaves
3. D. Wilks
4. Khaled H. Hamed
5. Sebastian Lerch
6. Stephan Rasp

Citations in the corpus

• Reliability of the CMIP3 ensemble (2010, methodology): “1000–2000km. This would imply at least 40 degrees of freedom for each data field, which motivates our choice of this value both here and for the idealised tests presented in section 3. Jolliffe and Primo [2008] suggest using 25 degrees of freedom for a single hemisphere, which is similar to our ch”
• Sampling distributions of the Brier score and Brier skill score under serial dependence (2010, methodology): “Elmore (2005) and Jolliffe and Primo (2008) have proposed more powerful alternatives to the conventional chi-square test for rank histogram uniformity.”