The Comparison and Evaluation of Forecasters.

Abstract

In this paper, we present methods for comparing and evaluating forecasters whose predictions are presented as their subjective probability distributions of various random variables that will be observed in the future, e.g. weather forecasters who each day must specify their own probabilities that it will rain in a particular location. We begin by reviewing the concepts of calibration and refinement, and describing the relationship between this notion of refinement and the notion of sufficiency in the comparison of statistical experiments. We also consider the question of interrelationships among forecasters and discuss methods by which an observer should combine the predictions from two or more different forecasters. Then we turn our attention to the concept of a proper scoring rule for evaluating forecasters, relating it to the concepts of calibration and refinement. Finally, we discuss conditions under which one forecaster can exploit the predictions of another forecaster to obtain a better score. (Author)

Open PDF

Document Details

Document Type
Technical Report
Publication Date
May 01, 1982
Accession Number
ADA121924

Entities

People

  • Morris H. Degroot
  • Stephen E. Fienberg

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Calibration
  • Data Science
  • Discriminant Analysis
  • Frequency
  • Information Science
  • Learning
  • Measurement
  • New York
  • Observers
  • Probability
  • Probability Distributions
  • Random Variables
  • Statistics
  • Stochastic Processes
  • Test And Evaluation
  • United States
  • Weather Forecasting

Readers

  • Atmospheric Science/Meteorology
  • Computational Modeling and Simulation
  • Systems Analysis and Design