Abstract
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.
Highlights
Lifetime experiments are conducted to collect data on items under study
In view of the above, this paper considers classical and Bayes estimation of the unknown parameters with some reliability characteristics for geometric distribution under randomly censored data
The geometric distribution belongs to the exponential family of distributions; most of the properties of maximum likelihood estimation (MLE) are valid in this case
Summary
The data are used for fitting a suitable lifetime model and inferring about the statistical properties and survival/reliability characteristics of the items These experiments may be expensive in terms of both cost and time. An electrical or electronic device such as bulb on test may break before its failure In such cases, the exact survival time (or time to event of interest) of the subjects is unknown; they are called randomly censored observations. In view of the above, this paper considers classical and Bayes estimation of the unknown parameters with some reliability characteristics for geometric distribution under randomly censored data. It is essential to mention here that we have used statistical software R [19] for computation purposes throughout the paper
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