Predictive model assessment for count data

Claudia Czado, Tilmann Gneiting, Leonhard Held

Research output: Contribution to journalArticlepeer-review

296 Scopus citations


We discuss tools for the evaluation of probabilistic forecasts and the critique of statistical models for count data. Our proposals include a nonrandomized version of the probability integral transform, marginal calibration diagrams, and proper scoring rules, such as the predictive deviance. In case studies, we critique count regression models for patent data, and assess the predictive performance of Bayesian age-period-cohort models for larynx cancer counts in Germany. The toolbox applies in Bayesian or classical and parametric or nonparametric settings and to any type of ordered discrete outcomes.

Original languageEnglish
Pages (from-to)1254-1261
Number of pages8
Issue number4
StatePublished - Dec 2009


  • Calibration
  • Forecast verification
  • Model diagnostics
  • Predictive deviance
  • Probability integral transform
  • Proper scoring rule


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