Title: Robust fitting of the binomial model
Authors : Ruckstuhl, Andreas
Welsh, A.H.
Published in : Annals of Statistics
Volume(Issue) : 29
Issue : 4
Pages : 1117
Pages to: 1136
Publisher / Ed. Institution : Institute of Mathematical Statistics
Issue Date: 2001
License (according to publishing contract) : Licence according to publishing contract
Type of review: Peer review (Publication)
Language : English
Subjects : Statistik; Binomial model; Robust fitting
Subject (DDC) : 500: Natural sciences and mathematics
Abstract: We consider the problem of robust inference for the binomial(m, I) 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 relationships between ro- bustness concepts and thereby provides new perspectives on these concepts.
Departement: School of Engineering
Organisational Unit: Institute of Data Analysis and Process Design (IDP)
Publication type: Article in scientific Journal
ISSN: 0090-5364
URI: http://www.jstor.org/stable/2674073
https://digitalcollection.zhaw.ch/handle/11475/7225
Appears in Collections:Publikationen School of Engineering

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