IMPROVING THE PRECISION OF ESTIMATES OF THE FREQUENCY OF RARE EVENTS

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The effect of prevalence and its interaction with sample size on the reliability of species distribution models (2009, background): “Values of 0.01 and 0.99 seem to be the lowest and highest workable thresholds; prevalences within these two extreme values will not present negative effects because of being unbalanced samples (see also Dixon et al. 2005).”
Assessing the effects of pseudo-absences on predictive distribution model performance (2008, methodology): “However, to avoid biases caused by the inclusion of an extremely high number of absences (King and Zeng, 2000; Dixon et al., 2005), we selected 10 times more absences than presences (1360) from each model.”
STATISTICS OF EXTREMES: MODELING ECOLOGICAL DISTURBANCES (2005, background): “Key words: generalized extreme value distribution; generalized Pareto distribution; paleoecology; peaks over threshold; return level; return period; sediment accumulation.”
IMPROVING INFERENCES IN POPULATION STUDIES OF RARE SPECIES THAT ARE DETECTED IMPERFECTLY (2005, methodology): “Dixon et al. (2005) use such an approach to improve estimates of capture efficiency of wasps by the insectivorous pitcher plant, Darlingtonia californica.”