Lower Confidence Limits on the Generalized Exponential Distribution Percentiles Under Progressive Type-I Interval Censoring |
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| Authors: | Ding-Geng Chen Y L Lio Nan Jiang |
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| Institution: | 1. Department of Biostatistics and Computational Biology, School of Nursing and School of Medicine and Dentistry , University of Rochester Medical Center , Rochester , USA;2. Jiann-Ping Hsu College of Public Health , Georgia Southern University , Statesboro , USA;3. Department of Mathematical Sciences , University of South Dakota , Vermilion , USA |
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| Abstract: | In industrial life test and survival analysis, the percentile estimation is always a practical issue with lower confidence bound required for maintenance purpose. Sampling distributions for the maximum likelihood estimators of percentiles are usually unknown. Bootstrap procedures are common ways to estimate the unknown sampling distributions. Five parametric bootstrap procedures are proposed to estimate the confidence lower bounds on maximum likelihood estimators for the generalized exponential (GE) distribution percentiles under progressive type-I interval censoring. An intensive simulation is conducted to evaluate the performances of proposed procedures. Finally, an example of 112 patients with plasma cell myeloma is given for illustration. |
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| Keywords: | Bootstrapping Maximum likelihood estimation Progressive type-I interval censoring |
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