NDLI: Minimum distance estimation in normed linear spaces with Donsker-classes

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Minimum distance estimation in normed linear spaces with Donsker-classes

Content Provider	Springer Nature Link
Author	Ferger, D.
Copyright Year	2010
Abstract	We consider minimum distance estimators where the discrepancy function is defined in terms of a supremum-norm based on a Donsker-class of functions. If the parameter set is contained in a normed linear space we prove a Portmanteau-type theorem. Here, the limit in general is not a probability measure, but an outer measure given by the hitting family of the set of all minimizing points of a certain stochastic process. In case there is exactly one minimizer one obtains traditional weak convergence.
Starting Page	246
Ending Page	266
Page Count	21
File Format	PDF
ISSN	10665307
Journal	Mathematical Methods of Statistics
Volume Number	19
Issue Number	3
e-ISSN	19348045
Language	English
Publisher	Allerton Press, Inc.
Publisher Date	2010-10-03
Publisher Place	Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	almost weak convergence argmin theorems Hadamard differentiability empirical process Donsker class Central limit and other weak theorems Exact distribution theory Functional limit theorems; invariance principles Asymptotic properties of estimators Statistical Theory and Methods
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Statistics, Probability and Uncertainty

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