Accession Number:

ADA478635

Title:

Predictive Model Assessment for Count Data

Descriptive Note:

Corporate Author:

WASHINGTON UNIV SEATTLE DEPT OF STATISTICS

Report Date:

2007-09-05

Pagination or Media Count:

20.0

Abstract:

We discuss tools for the evaluation of probabilistic forecasts and the critique of statistical models for ordered discrete data. Our proposals include a non-randomized 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. Key words Calibration Forecast veri cation Model diagnostics Predictive deviance Probability integral transform Proper scoring rule Ranked probability score.

Subject Categories:

  • Meteorology
  • Statistics and Probability

Distribution Statement:

APPROVED FOR PUBLIC RELEASE