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Abstract Details
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Activity Number:
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30
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Type:
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Contributed
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Date/Time:
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Sunday, July 29, 2012 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Computing
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| Abstract - #305060 |
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Title:
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The Generalized Lambda Distribution--Based Calibration Model
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Author(s):
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Wei Ning*+ and Steve Su
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Companies:
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Bowling Green State University and University of Western Australia
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Address:
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450 Msc, Bowling Green, OH, 43403, United States
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Keywords:
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Linear calibration model ;
Generalized Lambda Distribution ;
Skew normal distribution ;
Maximum likelihood estimation
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Abstract:
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In this paper, we propose a linear calibration model with the error terms followling a generalized lambda distribution(GLD) due to its broad flexibility. An algorithm is developed to obtain the maximum likelihood estimations of the parameters. Such a model is compared to the model with the traditional normality assumption and the skew normality assumption for the error terms. Simulations results show that the GLD calibration model is more flexible than the skew and the normal calibration model, especially dealing with the heavy tailed data. The proposed model is also applied to a real data to illustrate the estimation procedure.
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