Predictive Model Assessment for Count Data

被引：307

作者：

Czado, Claudia ^{[1
]}

Gneiting, Tilmann ^{[2
]}

Held, Leonhard ^{[3
]}

机构：

[1] Tech Univ Munich, Zentrum Math, D-85748 Garching, Germany

[2] Univ Washington, Dept Stat, Seattle, WA 98195 USA

[3] Univ Zurich, Biostat Abt, Inst Sozial & Praventivmed, CH-8001 Zurich, Switzerland

来源：

BIOMETRICS | 2009年 / 65卷 / 04期

基金：

美国国家科学基金会; 瑞士国家科学基金会;

关键词：

Calibration; Forecast verification; Model diagnostics; Predictive deviance; Probability integral transform; Proper scoring rule; TIME-SERIES; PROBABILISTIC FORECASTS; DISPERSION; RESIDUALS; CHOICE;

D O I：

10.1111/j.1541-0420.2009.01191.x

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

P>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.

引用

页码：1254 / 1261

页数：8

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