Predictive model assessment for count data
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Updated: Jun 23, 2026

An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Claudia Czado1, Tilmann Gneiting, Leonhard Held
1Zentrum Mathematik, Technische Universität München, Boltzmannstr. 3, D-85748 Garching, Germany.
Abstract:
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.
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