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Celebrating StatisticsPapers in honour of Sir David Cox on his 80th birthday$
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A. C. Davison, Yadolah Dodge, and N. Wermuth

Print publication date: 2005

Print ISBN-13: 9780198566540

Published to Oxford Scholarship Online: September 2007

DOI: 10.1093/acprof:oso/9780198566540.001.0001

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On semiparametric inference

On semiparametric inference

Chapter:
(p.115) 5 On semiparametric inference
Source:
Celebrating Statistics
Author(s):

Andrea Rotnitzky

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780198566540.003.0006

This chapter reviews some key elements of semiparametric theory and the contributions of semiparametric inference to the challenge posed by high-dimensional data, for which the specification of realistic parametric models for the mechanism generating the data may be difficult, if not impossible. The usefulness of semiparametric modeling is illustrated by a number of examples. A non-technical account of the formulation of the semiparametric variance bound and ways of calculating it are described. Consequences are set out for estimations that result from the curse of dimensionality. The possibilities for approaching inference are discussed when estimation of irregular parameters is inevitable; some unresolved questions are raised.

Keywords:   asymptotic theory, best limiting distribution, curse of dimensionality, efficient score, least favorable model, likelihood principle, robustness, variance bound

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