Journal article

Optimal unbiased estimation for maximal distribution

Abstract:: Unbiased estimation for parameters of maximal distribution is a fundamental problem in the statistical theory of sublinear expectations. In this paper, we proved that the maximum estimator is the largest unbiased estimator for the upper mean and the minimum estimator is the smallest unbiased estimator for the lower mean.

Publication status:: Published

Peer review status:: Peer reviewed

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APA Style

Jin, H., & Peng, S. (2021). Optimal unbiased estimation for maximal distribution. Probability, Uncertainty and Quantitative Risk, 6(3), 189–198.

MLA Style

Jin, H, and S Peng. “Optimal Unbiased Estimation for Maximal Distribution.” Probability, Uncertainty and Quantitative Risk, vol. 6, no. 3, 2021, pp. 189–98.

Chicago Style

Jin, H, and S Peng. 2021. “Optimal Unbiased Estimation for Maximal Distribution.” Probability, Uncertainty and Quantitative Risk 6 (3): 189–98.
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Files:: Jin_and_Peng_2021_Optimal_unbiased_estimation.pdf

(Preview, Accepted manuscript, pdf, 179.9KB, Terms of use)

Publisher copy:: 10.3934/puqr.2021009

Authors

+ Jin, H More by this author

Institution:: University of Oxford
Division:: MPLS
Department:: Mathematical Institute
Oxford college:: St Peter's College
Role:: Author
ORCID:: 0000-0001-5299-5730

+ Peng, S More by this author

Role:: Author

Publisher:: American Institute of Mathematical Sciences
Journal:: Probability, Uncertainty and Quantitative Risk More from this journal
Volume:: 6
Issue:: 3
Pages:: 189-198
Publication date:: 2021-09-23
Acceptance date:: 2021-08-10
DOI:: 10.3934/puqr.2021009
EISSN:: 2367-0126
ISSN:: 2095-9672

Language:: English
Keywords:: optimal unbiased estimation

sublinear expectations

maximal distributions
Pubs id:: 1345890
UUID:: uuid_61399c42-54c3-40ad-a38d-64b8ee875aa5
Local pid:: pubs:1345890
Source identifiers:: W3202446117
Deposit date:: 2026-01-21
ARK identifier:: ark:/29072/ora_61399c4254c340ada38d64b8ee875aa5

Terms of use

Copyright holder:: Shandong University and AIMS, LLC
Copyright date:: 2021
Rights statement:: ©2021 Shandong University and AIMS, LLC
Notes:: This is the accepted manuscript version of the article. The final version is available online from American Institute of Mathematical Sciences at https://dx.doi.org/10.3934/puqr.2021009

Licence:: Terms and Conditions of Use for Oxford University Research Archive

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