Maximum Likelihood Estimators on Manifolds View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2017-10-24

AUTHORS

Hatem Hajri , Salem Said , Yannick Berthoumieu

ABSTRACT

Maximum likelihood estimator (MLE) is a well known estimator in statistics. The popularity of this estimator stems from its asymptotic and universal properties. While asymptotic properties of MLEs on Euclidean spaces attracted a lot of interest, their studies on manifolds are still insufficient. The present paper aims to give a unified study of the subject. Its contributions are twofold. First it proposes a framework of asymptotic results for MLEs on manifolds: consistency, asymptotic normality and asymptotic efficiency. Second, it extends popular testing problems on manifolds. Some examples are discussed. More... »

PAGES

692-700

Book

TITLE

Geometric Science of Information

ISBN

978-3-319-68444-4
978-3-319-68445-1

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-68445-1_80

DOI

http://dx.doi.org/10.1007/978-3-319-68445-1_80

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1092381066


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