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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Bibliographic Details
Main Authors: Dimitriadis, Timo (Author) , Fissler, Tobias (Author) , Ziegel, Johanna (Author)
Format: Article (Journal)
Language:English
Published: 22 March 2023
In: Statistical papers
Year: 2023, Pages: 1-8
ISSN:1613-9798
DOI:10.1007/s00362-023-01428-x
Online Access: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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Author Notes:Timo Dimitriadis, Tobias Fissler, Johanna Ziegel
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Summary: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.
Item Description:Online veröffentlicht am 22. März 2023
Gesehen am 01.08.2023
Physical Description:Online Resource
ISSN:1613-9798
DOI:10.1007/s00362-023-01428-x