Analysis reveals that likelihood metrics outperform binned data methods for evaluating model performance, suggesting better approaches for empirical data.
Summary Likelihood, likelihood, likelihood (and maybe some other complicated approaches), but definitely not r^2 values from fitting regressions to binned data. A bit more nitty gritty detail In addition to causing issues with parameter estimation, binning based methods are also inappropriate when trying to determine which distribution provides the best fit to empirical data.
No takes yet. Share an insight, caveat, or question.
Ethan P. White (2009) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: