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Predictive Model Assessment for Count Data
Author(s) -
Czado Claudia,
Gneiting Tilmann,
Held Leonhard
Publication year - 2009
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2009.01191.x
Subject(s) - count data , statistics , bayesian probability , probabilistic logic , overdispersion , econometrics , parametric statistics , statistical model , computer science , deviance information criterion , regression analysis , mathematics , bayesian inference , poisson distribution
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.