Maximum Likelihood and Bayes Estimation in Randomly Censored Geometric Distribution
Joint Authors
Source
Journal of Probability and Statistics
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-02-21
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Krishna, Hare& Goel, Neha. 2017. Maximum Likelihood and Bayes Estimation in Randomly Censored Geometric Distribution. Journal of Probability and Statistics،Vol. 2017, no. 2017, pp.1-12.
https://search.emarefa.net/detail/BIM-1186271
Modern Language Association (MLA)
Krishna, Hare& Goel, Neha. Maximum Likelihood and Bayes Estimation in Randomly Censored Geometric Distribution. Journal of Probability and Statistics No. 2017 (2017), pp.1-12.
https://search.emarefa.net/detail/BIM-1186271
American Medical Association (AMA)
Krishna, Hare& Goel, Neha. Maximum Likelihood and Bayes Estimation in Randomly Censored Geometric Distribution. Journal of Probability and Statistics. 2017. Vol. 2017, no. 2017, pp.1-12.
https://search.emarefa.net/detail/BIM-1186271
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1186271