Please use this identifier to cite or link to this item: https://doi.org/10.1023/A:1009676116509
Title: Predicting a Future Lifetime through Box-Cox Transformation
Authors: Yang, Z. 
Keywords: Box-Cox transformation
Coverage probability
Kullback-Leibler information
Lifetime distributions
Prediction interval
Issue Date: 1999
Citation: Yang, Z. (1999). Predicting a Future Lifetime through Box-Cox Transformation. Lifetime Data Analysis 5 (3) : 265-279. ScholarBank@NUS Repository. https://doi.org/10.1023/A:1009676116509
Abstract: In predicting a future lifetime based on a sample of past lifetimes, the Box-Cox transformation method provides a simple and unified procedure that is shown in this article to meet or often outperform the corresponding frequentist solution in terms of coverage probability and average length of prediction intervals. Kullback-Leibler information and second-order asymptotic expansion are used to justify the Box-Cox procedure. Extensive Monte Carlo simulations are also performed to evaluate the small sample behavior of the procedure. Certain popular lifetime distributions, such as Weibull, inverse Gaussian and Birnbaum-Saunders are served as illustrative examples. One important advantage of the Box-Cox procedure lies in its easy extension to linear model predictions where the exact frequentist solutions are often not available.
Source Title: Lifetime Data Analysis
URI: http://scholarbank.nus.edu.sg/handle/10635/105303
ISSN: 13807870
DOI: 10.1023/A:1009676116509
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