Osband’s principle for identification functions
Given a statistical functional of interest such as the mean or median, a (strict) identification function is zero in expectation at (and only at) the true functional value. Identification functions are key objects in forecast validation, statistical estimation and dynamic modelling. For a possibly v...
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| Hauptverfasser: | , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
22 March 2023
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| In: |
Statistical papers
Year: 2023, Pages: 1-8 |
| ISSN: | 1613-9798 |
| DOI: | 10.1007/s00362-023-01428-x |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1007/s00362-023-01428-x Verlag, kostenfrei, Volltext: https://doi.org/https://link.springer.com/article/10.1007/s00362-023-01428-x 
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| Verfasserangaben: | Timo Dimitriadis, Tobias Fissler, Johanna Ziegel |
MARC
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| 520 | |a Given a statistical functional of interest such as the mean or median, a (strict) identification function is zero in expectation at (and only at) the true functional value. Identification functions are key objects in forecast validation, statistical estimation and dynamic modelling. For a possibly vector-valued functional of interest, we fully characterise the class of (strict) identification functions subject to mild regularity conditions. | ||
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