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Journal of Probability and Statistics
Volume 2017 (2017), Article ID 4860167, 12 pages
Research Article

Maximum Likelihood and Bayes Estimation in Randomly Censored Geometric Distribution

Department of Statistics, Ch. Charan Singh University, Meerut, India

Correspondence should be addressed to Hare Krishna

Received 25 July 2016; Revised 26 December 2016; Accepted 22 January 2017; Published 21 February 2017

Academic Editor: Hyungjun Cho

Copyright © 2017 Hare Krishna and Neha Goel. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In this article, we study the geometric distribution under randomly censored data. Maximum likelihood estimators and confidence intervals based on Fisher information matrix are derived for the unknown parameters with randomly censored data. Bayes estimators are also developed using beta priors under generalized entropy and LINEX loss functions. Also, Bayesian credible and highest posterior density (HPD) credible intervals are obtained for the parameters. Expected time on test and reliability characteristics are also analyzed in this article. To compare various estimates developed in the article, a Monte Carlo simulation study is carried out. Finally, for illustration purpose, a randomly censored real data set is discussed.