Evaluating Probabilistic Forecasts with scoringRules

Jordan, Alexander; Krüger, Fabian; Lerch, Sebastian (2019). Evaluating Probabilistic Forecasts with scoringRules. Journal of statistical software, 90(12) UCLA Statistics 10.18637/jss.v090.i12

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Probabilistic forecasts in the form of probability distributions over future events have become popular in several fields including meteorology, hydrology, economics, and demography. In typical applications, many alternative statistical models and data sources can be used to produce probabilistic forecasts. Hence, evaluating and selecting among competing methods is an important task. The scoringRules package for R provides functionality for comparative evaluation of probabilistic models based on proper scoring rules, covering a wide range of situations in applied work. This paper discusses implementation and usage details, presents case studies from meteorology and economics, and points to the relevant background literature.

Item Type:

Journal Article (Original Article)

Division/Institute:

08 Faculty of Science > Department of Mathematics and Statistics > Institute of Mathematical Statistics and Actuarial Science

UniBE Contributor:

Jordan, Alexander Inigo

Subjects:

500 Science > 510 Mathematics

ISSN:

1548-7660

Publisher:

UCLA Statistics

Language:

English

Submitter:

Alexander Inigo Jordan

Date Deposited:

14 Apr 2020 16:08

Last Modified:

14 Apr 2020 16:08

Publisher DOI:

10.18637/jss.v090.i12

BORIS DOI:

10.7892/boris.142917

URI:

https://boris.unibe.ch/id/eprint/142917

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