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Activity Number: 30
Type: Contributed
Date/Time: Sunday, July 29, 2012 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Computing
Abstract - #305060
Title: The Generalized Lambda Distribution--Based Calibration Model
Author(s): Wei Ning*+ and Steve Su
Companies: Bowling Green State University and University of Western Australia
Address: 450 Msc, Bowling Green, OH, 43403, United States
Keywords: Linear calibration model ; Generalized Lambda Distribution ; Skew normal distribution ; Maximum likelihood estimation

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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