Predictive model assessment for count data.
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.
Zentrum Mathematik, Technische Universität München, Boltzmannstr. 3, D-85748 Garching, Germany.
Claudia
C
Czado
Tilmann
T
Gneiting
Leonhard
L
Held
D001499:Bayes Theorem; D001699:Biometry / Q000379:methods*; D015331:Cohort Studies; D005858:Germany / Q000453:epidemiology; D006801:Humans; D007822:Laryngeal Neoplasms / Q000453:epidemiology; D015233:Models, Statistical; D012044:Regression Analysis; D018709:Statistics, Nonparametric