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Predictive Model Assessment for Count Data


Czado, C; Gneiting, T; Held, L (2009). Predictive Model Assessment for Count Data. Biometrics, 65(4):1254-1261.

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.

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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100 citations in Web of Science®
101 citations in Scopus®
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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Epidemiology, Biostatistics and Prevention Institute (EBPI)
Dewey Decimal Classification:610 Medicine & health
Language:English
Date:December 2009
Deposited On:22 Jan 2010 08:52
Last Modified:05 Apr 2016 13:43
Publisher:Wiley-Blackwell
ISSN:0006-341X
Publisher DOI:https://doi.org/10.1111/j.1541-0420.2009.01191.x

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