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Truncation and censoring are often observed in lifetime data, the data condition will be more complex in the railway wagon industry due to the observational constraints, periodic inspection, and new model updating. In this paper, we extract the data scenarios from actual problems, then generate the data under different censoring scenarios through Monte Carlo method, the maximum likelihood estimation method is employed to get the numerical solutions. Certain evaluation indexes are used to determine the tendency of estimation bias. Model discrimination is also studied to describe the influence by misuse. Finally, suggestions to further improve the accuracy of lifetime models are provided.
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Qi et al. (2021) studied this question.
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