%0 Journal Article %1 jordan2019evaluating %A Jordan, Alexander %A Krüger, Fabian %A Lerch, Sebastian %D 2019 %I Foundation for Open Access Statistic %J Journal of Statistical Software %K MyYangtzeWork skill software statistics verification %N 12 %R 10.18637/jss.v090.i12 %T Evaluating Probabilistic Forecasts with scoringRules %U https://doi.org/10.18637/jss.v090.i12 %V 90 %X t: 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.

@article{jordan2019evaluating, abstract = {t: 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. }, added-at = {2020-06-04T15:42:52.000+0200}, author = {Jordan, Alexander and Krüger, Fabian and Lerch, Sebastian}, biburl = {https://www.bibsonomy.org/bibtex/2d31b64d4dddcea07e3949802c4e7d488/pbett}, description = {Evaluating Probabilistic Forecasts with scoringRules | Jordan | Journal of Statistical Software}, doi = {10.18637/jss.v090.i12}, interhash = {5d341201763e45f02138766aef72846a}, intrahash = {d31b64d4dddcea07e3949802c4e7d488}, journal = {Journal of Statistical Software}, keywords = {MyYangtzeWork skill software statistics verification}, number = 12, publisher = {Foundation for Open Access Statistic}, timestamp = {2020-06-04T15:42:52.000+0200}, title = {Evaluating Probabilistic Forecasts with scoringRules}, url = {https://doi.org/10.18637/jss.v090.i12}, volume = 90, year = 2019 }