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Estimating a probability mass function with unknown labels

Abstract:

In the context of a species sampling problem, we discuss a nonparametric maximum likelihood estimator for the underlying probability mass function. The estimator is known in the computer science literature as the high profile estimator. We prove strong consistency and derive the rates of convergence, for an extended model version of the estimator. We also study a sieved estimator for which similar consistency results are derived. Numerical computation of the sieved estimator is of great inter...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Publisher's Version

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Publisher copy:
10.1214/17-AOS1542

Authors


Anevski, D More by this author
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Institution:
University of Oxford
Division:
MPLS Division
Department:
Department of Engineering Science
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Funding agency for:
Zohren, S
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Funding agency for:
Zohren, S
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Funding agency for:
Zohren, S
Publisher:
Institute of Mathematical Statistics Publisher's website
Journal:
The Annals of Statistics Journal website
Volume:
45
Issue:
6
Pages:
2708-2735
Publication date:
2017-12-15
Acceptance date:
2016-05-23
DOI:
EISSN:
2168-8966
ISSN:
0090-5364
Pubs id:
pubs:441614
URN:
uri:6b36f895-96b3-42e7-8fcf-ebe9a296eca4
UUID:
uuid:6b36f895-96b3-42e7-8fcf-ebe9a296eca4
Local pid:
pubs:441614

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