Inferences for two Lindley populations based on joint progressive type-II censored data

Abstract

The joint censoring scheme is of great importance when the motive of study is to compare the relative merits of products in relation of their service times. In last few years, progressive censoring received considerable attention in order to save cost and time of the experiment. This paper deals with inferences for Lindley populations, when joint progressive type-II censoring scheme is applied on two samples in a joint manner. Here, the maximum likelihood estimators of parameters are derived along with their associated confidence intervals which are dependent on the Fisher’s information matrix. The boot-p and boot-t confidence intervals are also obtained. Bayes estimators of the unknown parameters assuming gamma priors are calculated. The concept of importance sampling technique and Gibbs sampling technique are used as the Bayes estimators cannot be calculated in closed form. HPD credible intervals are also constructed. A Monte Carlo simulation study is performed to measure the efficiency of the estimates. A real data set is given for illustrative purpose. Finally some criteria for an optimum censoring scheme are discussed.

Keywords:

• Bayes estimation
• Generalized entropy loss function
• Joint progressive type-II censoring
• Lindley distribution
• Optimum censoring scheme

MATHEMATICS SUBJECT CLASSIFICATION:

• 62N01
• 62N02
• 62F10
• 62F15

Acknowledgements

We are thankful to the unknown learned reviewers and the associated editor for their valuable comments and suggestions, because of which this manuscript could be improved considerably in the revised version.