- 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...
Expand abstract - Publication status:
- Published
- Peer review status:
- Peer reviewed
- Version:
- Publisher's Version
- Files:
-
-
(pdf, 237.8kb)
-
- Publisher copy:
- 10.1214/17-AOS1542
-
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- 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
- Copyright holder:
- Institute of Mathematical Statistics
- Copyright date:
- 2017
- Notes:
- © Institute of Mathematical Statistics, 2017. This is the publisher's version of the article which is available online from Oxford University Press at: https://projecteuclid.org/euclid.aos/1513328588
Journal article
Estimating a probability mass function with unknown labels
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