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

Authors

  • Randhir Singh

Bayes Estimator, Loss Function, Risk Function, Binomial Distribution

Abstract

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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How to Cite

On Baysian Estimation of Loss of Estimators of Unknown Parameter of Binomial Distribution. (2022). Global Journal of Science Frontier Research, 22(F4), 37-40. https://doi.org/10.34257/GJSFRFVOL22IS4PG37

 

References

(2022) Unknown Title.

J Berger (1985) The frequentist viewpoint and conditioning. 15-44.

Guobing Fan (2016) Estimation of the Loss and Risk Functions of parameter of Maxwell's distribution. 4(4), 129-133.

J Keifer (1977) Conditional Confidence Statements and Confidence Estimators. 72(360), 789.

Randhir Singh (2021) On Bayesian Estimation of Loss and Risk Functions. 9(3), 73-77.

Andrew Rukhin (1988) Estimating the Loss of Estimators of a Binomial Parameter. 75(1), 153.

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

Published

2022-11-01

Issue

Vol. 22 No. F4 (2022): GJSFR-F Mathematics: Volume 22 Issue F4

Section

Articles

License

Copyright (c) 2022 Authors and Global Journals Private Limited

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

On Baysian Estimation of Loss of Estimators of Unknown Parameter of Binomial Distribution. (2022). Global Journal of Science Frontier Research, 22(F4), 37-40. https://doi.org/10.34257/GJSFRFVOL22IS4PG37