On Baysian Estimation of Loss of Estimators of Unknown Parameter of Binomial Distribution

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Randhir Singh
Randhir Singh

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GJSFR Volume 22 Issue F4

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This paper aims at the Bayesian estimation for the loss and risk functions of the unknown parameter of the binomial distribution under the loss function which is different from that given by Rukhin (1988). The estimation involves beta distribution, a natural conjugate prior density function for the unknown parameter. Estimators obtained are conservatively biased and have finite frequentist risk.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

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Not applicable for this article.

Randhir Singh. 2026. \u201cOn Baysian Estimation of Loss of Estimators of Unknown Parameter of Binomial Distribution\u201d. Global Journal of Science Frontier Research - F: Mathematics & Decision GJSFR-F Volume 22 (GJSFR Volume 22 Issue F4): .

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Bayesian estimation of unknown binomial parameters for improved statistical analysis.
Issue Cover
GJSFR Volume 22 Issue F4
Pg. 37- 40
Journal Specifications

Crossref Journal DOI 10.17406/GJSFR

Print ISSN 0975-5896

e-ISSN 2249-4626

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GJSFR-F Classification: DDC Code: 843.7 LCC Code: PQ2165.C5
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v1.2

Issue date

November 1, 2022

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English

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This paper aims at the Bayesian estimation for the loss and risk functions of the unknown parameter of the binomial distribution under the loss function which is different from that given by Rukhin (1988). The estimation involves beta distribution, a natural conjugate prior density function for the unknown parameter. Estimators obtained are conservatively biased and have finite frequentist risk.

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On Baysian Estimation of Loss of Estimators of Unknown Parameter of Binomial Distribution

Randhir Singh
Randhir Singh

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