|Title:||Robust fitting of the binomial model|
|Authors :||Ruckstuhl, Andreas|
|Published in :||Annals of Statistics|
|Publisher / Ed. Institution :||Institute of Mathematical Statistics|
|License (according to publishing contract) :||Licence according to publishing contract|
|Type of review:||Peer review (Publication)|
|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|
|Appears in Collections:||Publikationen School of Engineering|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.