Predictive Model Assessment for Count Data

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.

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Document Details

Document Type
Technical Report
Publication Date
Sep 05, 2007
Accession Number
ADA478635

Entities

People

  • Claudia Czado
  • Leonhard Held
  • Tilmann Gneiting

Organizations

  • University of Washington

Tags

DTIC Thesaurus Topics

  • Age Groups
  • Bayesian Networks
  • Case Studies
  • Computational Science
  • Data Science
  • Distribution Functions
  • Information Science
  • Integral Transforms
  • Knowledge Management
  • Monte Carlo Method
  • Predictive Modeling
  • Probability
  • Probability Distributions
  • Social Sciences
  • Standards
  • Statistical Analysis
  • Statistics

Fields of Study

  • Mathematics

Readers

  • Calculus or Mathematical Analysis
  • Regression Analysis.
  • Speech Processing/Speech Recognition.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference