Optimum Plans for Progressive Censored Competing Risk Data Under Kies Distribution

Prakash Chandra, Chandrakant Lodhi and Yogesh Mani Tripathi ()
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Prakash Chandra: Indian Institute of Technology Patna
Chandrakant Lodhi: Centre of Rajiv Gandhi Institute of Petroleum Technology
Yogesh Mani Tripathi: Indian Institute of Technology Patna

Sankhya B: The Indian Journal of Statistics, 2024, vol. 86, issue 1, No 1, 40 pages

Abstract: Abstract This paper considers optimal inference for the competing risks model when the latent failure times follow the two-parameter Kies distribution with a common shape parameter. We obtain different optimum schemes for competing risks model under progressive type-II censoring scheme. The existence and uniqueness properties of maximum likelihood estimates of parameters are derived. Further observed and expected Fisher information matrices are evaluated. In sequel approximate intervals of Kies parameters are computed. A simulation study has been used to evaluate proposed estimators. Analysis of a real data set is presented as well, for illustration purpose. Furthermore, we obtain optimal censoring plans by minimizing the experimental cost and variance associated with the estimators by considering single as well as multi-objective frameworks.

Keywords: Competing risks model; Progressive type-II censoring; Maximum likelihood estimate; Likelihood ratio test; Optimal progressive censoring plan; Primary 62F10; Secondary 62N01 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s13571-023-00315-7

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