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May 2021 A Bayesian nonparametric estimation to entropy
Luai Al-Labadi, Vishakh Patel, Kasra Vakiloroayaei, Clement Wan
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Braz. J. Probab. Stat. 35(2): 421-434 (May 2021). DOI: 10.1214/20-BJPS483

Abstract

A Bayesian nonparametric estimator to entropy is proposed. The derivation of the new estimator relies on using the Dirichlet process and adapting the well-known frequentist estimators of Vasicek (Journal of Royal Statistical Society B 38 (1976) 54–59) and Ebrahimi, Pflughoeft and Soofi (Statistics & Probability Letters 20 (1994) 225–234). Several theoretical properties, such as consistency, of the proposed estimator are obtained. The quality of the proposed estimator has been investigated through several examples, in which it exhibits excellent performance.

Acknowledgments

The authors are grateful to the Editor, the Associate Editor and the reviewer for their comments and suggestions that have greatly improved the paper.

Citation

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Luai Al-Labadi. Vishakh Patel. Kasra Vakiloroayaei. Clement Wan. "A Bayesian nonparametric estimation to entropy." Braz. J. Probab. Stat. 35 (2) 421 - 434, May 2021. https://doi.org/10.1214/20-BJPS483

Information

Received: 1 March 2020; Accepted: 1 July 2020; Published: May 2021
First available in Project Euclid: 24 March 2021

Digital Object Identifier: 10.1214/20-BJPS483

Rights: Copyright © 2021 Brazilian Statistical Association

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Vol.35 • No. 2 • May 2021
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