Characterizing M-estimators

We characterize the full classes of M-estimators for semiparametric models of general functionals by formally connecting the theory of consistent loss functions from forecast evaluation with the theory of M-estimation. This novel characterization result allows us to leverage existing results on loss...

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Bibliographic Details
Main Authors: Dimitriadis, Timo (Author) , Fissler, Tobias (Author) , Ziegel, Johanna (Author)
Format: Article (Journal)
Language:English
Published: 8 May 2023
In: Biometrika
Year: 2023, Pages: 1-8
ISSN:1464-3510
DOI:10.1093/biomet/asad026
Online Access:Verlag, kostenfrei, Volltext: https://doi.org/10.1093/biomet/asad026
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Author Notes:by Timo Dimitriadis, Tobias Fissler and Johanna Ziegel
Description
Summary:We characterize the full classes of M-estimators for semiparametric models of general functionals by formally connecting the theory of consistent loss functions from forecast evaluation with the theory of M-estimation. This novel characterization result allows us to leverage existing results on loss functions known from the literature on forecast evaluation in estimation theory. We exemplify advantageous implications for the fields of robust, efficient, equivariant and Pareto-optimal M-estimation.
Item Description:Gesehen am 01.08.2023
Physical Description:Online Resource
ISSN:1464-3510
DOI:10.1093/biomet/asad026