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On a progressively censored competing risks data from Gompertz distribution.
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- Author(s): Lodhi, Chandrakant; Tripathi, Yogesh Mani; Bhattacharya, Ritwik
- Source:
Communications in Statistics: Simulation & Computation; 2023, Vol. 52 Issue 4, p1278-1299, 22p- Subject Terms:
- Source:
- Additional Information
- Abstract: We study a competing risks model using Gompertz distribution under progressive Type-II censoring when probability distributions of failure causes are identically distributed with common scale and different shape parameters. Maximum likelihood estimates (MLEs) of these parameters are obtained and their uniqueness and existence behavior are also discussed. The asymptotic intervals are derived from the observed Fisher information matrix. We compare the performance of all the estimators numerically using simulations. Analysis of a real data set is presented as well. We further determine optimal censoring scheme using expected Fisher information matrix. The design parameters are selected based on suitable measures like cost-based and variance-based criteria functions. Finally, we discuss single and multi-objective optimization approaches to find optimal censoring schemes. [ABSTRACT FROM AUTHOR]
- Abstract: Copyright of Communications in Statistics: Simulation & Computation is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Abstract:
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