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Robust fitting of the binomial model

Record type: Article

We consider the problem of robust inference for the binomial$(m,\pi)$ model. The discreteness of the data and the fact that the parameter and sample spaces are bounded mean that standard robustness theory gives surprising results. For example, the maximum likelihood estimator (MLE) is quite robust, it cannot be improved on for $m=1$ but can be for $m>1$. We discuss four other classes of estimators: M-estimators, minimum disparity estimators, optimal MGP estimators, and a new class of estimators which we call E-estimators. We show that E-estimators have a non-standard asymptotic theory which challenges the accepted relationship between robustness concepts and thereby provided new perspectives on these concepts.

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Citation

Ruckstuhl, A.F. and Welsh, A.H. (2001) Robust fitting of the binomial model Annals of Statistics, 29, (4), pp. 1117-1136.

More information

Published date: 2001
Keywords: bias, breakdown point, e-estimation, influence function, likelihood disparity, m-estimation, minimum disparity estimation, optimal MGP estimation
Organisations: Statistics

Identifiers

Local EPrints ID: 29935
URI: http://eprints.soton.ac.uk/id/eprint/29935
PURE UUID: 7a8cc791-f0cf-4775-937f-c7a37979f056

Catalogue record

Date deposited: 11 May 2006
Last modified: 17 Jul 2017 15:56

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Contributors

Author: A.F. Ruckstuhl
Author: A.H. Welsh

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