Predictive Model Assessment for Count Data |
| |
| Authors: | Claudia Czado Tilmann Gneiting Leonhard Held |
| |
| Institution: | 1. Zentrum Mathematik, Technische Universit?t München, Boltzmannstr. 3, D‐85748 Garching, Germany;2. Department of Statistics, University of Washington, Box 354322, Seattle, Washington 98195, U.S.A.;3. Institut für Sozial‐ und Pr?ventivmedizin, Abteilung Biostatistik, Universit?t Zürich, Hirschengraben 84, CH‐8001 Zürich, Switzerland |
| |
| Abstract: | Summary 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. |
| |
| Keywords: | Calibration Forecast verification Model diagnostics Predictive deviance Probability integral transform Proper scoring rule |
|
|
