Transformation Approaches for the Construction of Weibull Prediction Interval

Two methods of transforming the Weibull data to near normality, namely the Box-Cox method and Kullback-Leibler (KL) information method, are discussed and contrasted. A simple prediction interval (PI) based on the better KL information method is proposed. The asymptotic property of this interval is e...

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Main Authors: YANG, Zhenlin, SEE, Stanley P., XIE, Min
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2003
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Online Access:https://ink.library.smu.edu.sg/soe_research/196
https://ink.library.smu.edu.sg/context/soe_research/article/1195/viewcontent/Transformation_Weibull_prediction_2003.pdf
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spelling sg-smu-ink.soe_research-11952018-05-07T06:48:56Z Transformation Approaches for the Construction of Weibull Prediction Interval YANG, Zhenlin SEE, Stanley P. XIE, Min Two methods of transforming the Weibull data to near normality, namely the Box-Cox method and Kullback-Leibler (KL) information method, are discussed and contrasted. A simple prediction interval (PI) based on the better KL information method is proposed. The asymptotic property of this interval is established. Its small sample behavior is investigated using Monte Carlo simulation. Simulation results show that this simple interval is close to the existing complicated PI where the percentage points of the reference distribution have to be either simulated or approximated. 2003-07-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/soe_research/196 info:doi/10.1016/s0167-9473(02)00232-3 https://ink.library.smu.edu.sg/context/soe_research/article/1195/viewcontent/Transformation_Weibull_prediction_2003.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University Box-Cox transformation Coverage probability Kullback-Leibler information Prediction interval Weibull distribution Econometrics
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Box-Cox transformation
Coverage probability
Kullback-Leibler information
Prediction interval
Weibull distribution
Econometrics
spellingShingle Box-Cox transformation
Coverage probability
Kullback-Leibler information
Prediction interval
Weibull distribution
Econometrics
YANG, Zhenlin
SEE, Stanley P.
XIE, Min
Transformation Approaches for the Construction of Weibull Prediction Interval
description Two methods of transforming the Weibull data to near normality, namely the Box-Cox method and Kullback-Leibler (KL) information method, are discussed and contrasted. A simple prediction interval (PI) based on the better KL information method is proposed. The asymptotic property of this interval is established. Its small sample behavior is investigated using Monte Carlo simulation. Simulation results show that this simple interval is close to the existing complicated PI where the percentage points of the reference distribution have to be either simulated or approximated.
format text
author YANG, Zhenlin
SEE, Stanley P.
XIE, Min
author_facet YANG, Zhenlin
SEE, Stanley P.
XIE, Min
author_sort YANG, Zhenlin
title Transformation Approaches for the Construction of Weibull Prediction Interval
title_short Transformation Approaches for the Construction of Weibull Prediction Interval
title_full Transformation Approaches for the Construction of Weibull Prediction Interval
title_fullStr Transformation Approaches for the Construction of Weibull Prediction Interval
title_full_unstemmed Transformation Approaches for the Construction of Weibull Prediction Interval
title_sort transformation approaches for the construction of weibull prediction interval
publisher Institutional Knowledge at Singapore Management University
publishDate 2003
url https://ink.library.smu.edu.sg/soe_research/196
https://ink.library.smu.edu.sg/context/soe_research/article/1195/viewcontent/Transformation_Weibull_prediction_2003.pdf
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