Second-order asymptotically optimal statistical classification-Reference-Cited by-同舟云学术

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Second-order asymptotically optimal statistical classification

Published:2019-01-22 Issue: Volume: Page:
ISSN:2049-8764
Container-title:Information and Inference: A Journal of the IMA
language:en
Short-container-title:

Author:

Zhou Lin¹^ORCID,Tan Vincent Y F²,Motani Mehul³

Affiliation:

1. Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor

2. Department of Electrical and Computer Engineering and Department of Mathematics, National University of Singapore

3. Department of Electrical and Computer Engineering, National University of Singapore

Funder

National University of Singapore

Singapore National Research Foundation

Publisher

Oxford University Press (OUP)

Subject

Applied Mathematics,Computational Theory and Mathematics,Numerical Analysis,Statistics and Probability,Analysis

Link

http://academic.oup.com/imaiai/advance-article-pdf/doi/10.1093/imaiai/iay023/27580639/iay023.pdf

Reference28 articles.

1. Sublinear algorithms for outlier detection and generalized closeness testing;Acharya;IEEE ISIT,2014

2. Testing closeness of discrete distributions;Batu;J. ACM,2013

3. The accuracy of the Gaussian approximation to the sum of independent variates;Berry;Trans. Amer. Math. Soc.,1941

4. Hypothesis testing and information theory;Blahut;IEEE Trans. Inf. Theory,1974

5. Convex Optimization

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1. Universal Neyman–Pearson classification with a partially known hypothesis;Information and Inference: A Journal of the IMA;2024-04-01

2. On Universal Sequential Classification from Sequentially Observed Empirical Statistics;2022 IEEE Information Theory Workshop (ITW);2022-11-01

3. Second-Order Asymptotically Optimal Outlier Hypothesis Testing;IEEE Transactions on Information Theory;2022-06

4. Analysis on Optimal Error Exponents of Binary Classification for Source with Multiple Subclasses;Entropy;2022-04-30

5. Second-Order Asymptotically Optimal Outlying Sequence Detection with Reject Option;2021 IEEE Information Theory Workshop (ITW);2021-10-17

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