Uncertainty and Inference for Verification Measures

By Ian T. Jolliffe. 2007

109 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/103f614ddd0b6a6996284a1001c09c56ff12cc8e

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Citations in the corpus

Reliability diagrams for non‐parametric density forecasts of continuous variables: Accounting for serial correlation (2010, methodology): “induce different types of dependence structure in the timeseries {zt,k} of probability integral transforms. The surrogate consistency resampling method belongs to the more general class of resampling methods for dependent data. For an overview of those methods, see e.g. Lahiri (2003). An important a”
A Probabilistic Derivation of Heidke Skill Score (2014, background): “This conceptual difference between derivations can be seen as the difference between frequentist and Bayesian approaches (e.g., Jolliffe 2007).”
Strictly Proper Scoring Rules, Prediction, and Estimation (2007, background): “Diebold and Mariano (1995), Hamill (1999), Briggs (2005), Briggs and Ruppert (2005), and Jolliffe (2006) have developed formal tests of forecast performance, skill, and value.”
Detecting Improvements in Forecast Correlation Skill: Statistical Testing and Power Analysis (2017, methodology): “As pointed out by Jolliffe (2007): ‘‘The value of a verification measure on its own is of little use; it also”
Sampling Uncertainty and Confidence Intervals for the Brier Score and Brier Skill Score (2008, methodology): “…such as the probability of detection or hit rate (Kane and Brown 2000; Jolliffe 2007), odds ratio (Stephenson 2000), false alarm rate and skill scores (Thornes and Stephenson 2001), the correlation coefficient (Jolliffe 2007), and relative operating characteristics (Mason and Graham 2002).”
Significance of changes in medium-range forecast scores (2016, background): “Statistical significance testing is becoming more widespread in meteorology (Ebert et al., 2013) and the statistical background is given by Jolliffe (2007) while textbooks cover all the fundamentals (von Storch and Zwiers, 1999; Wilks, 2006).”
Stochastic Model Output Statistics for Bias Correcting and Downscaling Precipitation Including Extremes (2014, methodology): “(7) 2) DOWNSCALING PRECIPITATION INTENSITIES To model the influence of our predictor, RCMsimulated precipitation xi, on the parameters of the mixture model, we employ a vector generalized linear model (VGLM) as regressionmodel (Yee andWild 1996; Yee and Stephenson 2007).”
Impact of Stochastic Physics in a Convection-Permitting Ensemble (2012, background): “Except where indicated, all score differences mentioned in this work are significant in this sense, which means that the score differences are unlikely to be mere sampling artifacts (Jolliffe, 2007) is a good introduction to bootstrap confidence testing).”