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  Bayesian Estimators for Robins-Ritov’s Problem

Harmeling, S.(2007). Bayesian Estimators for Robins-Ritov’s Problem (EDI-INF-RR-1189).

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 Urheber:
Harmeling, S1, Autor           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 Zusammenfassung: Bayesian or likelihood-based approaches to data analysis became very popular in the 64257;eld of Machine Learning. However, there exist theoretical results which question the general applicability of such approaches; among those a result by Robins and Ritov which introduce a speci64257;c example for which they prove that a likelihood-based estimator will fail (i.e. it does for certain cases not converge to a true parameter estimate, even given in64257;nite data). In this paper we consider various approaches to formulate likelihood-based estimators in this example, basically by considering various extensions of the presumed generative model of the data. We can derive estimators which are very similar to the classical Horvitz-Thompson and which also account for a priori knowledge of an observation probability function.

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 Datum: 2007-10
 Publikationsstatus: Erschienen
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 Ort, Verlag, Ausgabe: -
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 Identifikatoren: Reportnr.: EDI-INF-RR-1189
BibTex Citekey: 6326
 Art des Abschluß: -

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