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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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171 citations in Web of Science®
178 citations in Scopus®
220 citations in Microsoft Academic
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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
Scopus Subject Areas:Physical Sciences > Statistics and Probability
Life Sciences > General Biochemistry, Genetics and Molecular Biology
Life Sciences > General Immunology and Microbiology
Life Sciences > General Agricultural and Biological Sciences
Physical Sciences > Applied Mathematics
Language:English
Date:December 2009
Deposited On:22 Jan 2010 08:52
Last Modified:29 Jul 2020 20:46
Publisher:Wiley-Blackwell
ISSN:0006-341X
OA Status:Closed
Publisher DOI:https://doi.org/10.1111/j.1541-0420.2009.01191.x

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