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Erschienen in:
1988 | OriginalPaper | Buchkapitel
Introduction
verfasst von : D. L. McLeish, Christopher G. Small
Erschienen in: The Theory and Applications of Statistical Inference Functions
Verlag: Springer New York
Enthalten in: Professional Book Archive
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The theory of estimating functions has become of interest in a wide variety of statistical applications, partly because it has a number of virtues in common with methods such as maximum likelihood estimation while possessing sufficient flexibility to tackle problems where maximum likelihood fails, such as the Neyman-Scott paradox. In this monograph we present a self-contained development with a number of applications to estimation, censoring, robustness and inferential separation of parameters.