Predicting a Future Median Life through a Power Transformation

A simple and unified prediction interval (PI) for the median of a future lifetime can be obtained through a power transformation. This interval usually possesses the correct coverage, at least asymptotically, when the transformation is known. However, when the transformation is unknown and is estima...

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Main Author: YANG, Zhenlin
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Language:English
Published: Institutional Knowledge at Singapore Management University 2001
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Online Access:https://ink.library.smu.edu.sg/soe_research/509
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spelling sg-smu-ink.soe_research-15082010-09-23T05:48:03Z Predicting a Future Median Life through a Power Transformation YANG, Zhenlin A simple and unified prediction interval (PI) for the median of a future lifetime can be obtained through a power transformation. This interval usually possesses the correct coverage, at least asymptotically, when the transformation is known. However, when the transformation is unknown and is estimated from the data, a correction is required. A simple correction factor is derived based on large sample theory. Simulation shows that the unified PI after correction performs well. When compared with the existing frequentist PI's, it shows an equivalent or a better performance in terms of coverage probability and average length of the interval. Its nonparametric aspect and the ease of usage make it very attractive to practitioners. Real data examples are provided for illustration. 2001-01-01T08:00:00Z text https://ink.library.smu.edu.sg/soe_research/509 Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University Economics
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Economics
spellingShingle Economics
YANG, Zhenlin
Predicting a Future Median Life through a Power Transformation
description A simple and unified prediction interval (PI) for the median of a future lifetime can be obtained through a power transformation. This interval usually possesses the correct coverage, at least asymptotically, when the transformation is known. However, when the transformation is unknown and is estimated from the data, a correction is required. A simple correction factor is derived based on large sample theory. Simulation shows that the unified PI after correction performs well. When compared with the existing frequentist PI's, it shows an equivalent or a better performance in terms of coverage probability and average length of the interval. Its nonparametric aspect and the ease of usage make it very attractive to practitioners. Real data examples are provided for illustration.
format text
author YANG, Zhenlin
author_facet YANG, Zhenlin
author_sort YANG, Zhenlin
title Predicting a Future Median Life through a Power Transformation
title_short Predicting a Future Median Life through a Power Transformation
title_full Predicting a Future Median Life through a Power Transformation
title_fullStr Predicting a Future Median Life through a Power Transformation
title_full_unstemmed Predicting a Future Median Life through a Power Transformation
title_sort predicting a future median life through a power transformation
publisher Institutional Knowledge at Singapore Management University
publishDate 2001
url https://ink.library.smu.edu.sg/soe_research/509
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