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Title
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Type-II progressive censoring with GLM-based random removal mechanism dependent on the experimental conditions
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Type
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Refereeing
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Keywords
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Type-II progressive censoring; Stochastic Dependent Random Removal; Generalized Linear Models; Proportional Hazard Rate Family; Expected Experiment Time
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Abstract
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This article presents a novel stochastic removal mechanism under Type-II progressive random censoring in which removal probabilities are allowed to be dependent on the lifetime conditions through Generalized Linear Models. These conditions potentially include failure distances (the length of time necessary for observing the next failure) or other covariate information available in the experiment. The proposed GLM-based random removal mechanism includes a set of tuning parameters to be determined by the researcher according to the goals of study. These parameters allow flexible determination of the removal probabilities leading to necessery experimental cost and time reductions. To establish the proposed mechanism, the Proportional Hazard Rate family of distributions is considered. Also, the maximum likelihood estimators of the parameters and their asymptotic variances are derived for Weibull distributed lifetime data. A simple simulation algorithm for generating Type-II progressively censored samples with GLM-based dependent removal probabilities is also presented. The expected experiment time required to complete the life test under this censoring scheme is also investigated using a Monte Carlo integration method. Several simulation studies are conducted to evaluate and compare the performance of the proposed mechanism. Finally, a real life example is analyzed for illustrative purposes.
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Researchers
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Nooshin Hakamipour (Referee)
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