Abstract Details
Activity Number:
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193
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Type:
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Contributed
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Date/Time:
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Monday, August 10, 2015 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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Abstract #314735
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Title:
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Skew T and Semiparametric Empirical Likelihoods Versus Parametric Robust Likelihood
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Author(s):
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Wei-Cheng Hsiao* and Tsung-Shan Tsou
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Companies:
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Institute of Statistical Science, Academia Sinica and National Central University
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Keywords:
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skew t distribution ;
empirical likelihood ;
robust likelihood ;
model misspecification
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Abstract:
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Likelihood is certainly one of the most important entities for inference. The skew t is a popular distribution that researchers employ to analyze asymmetric data. Our findings, however, reveal that the skew t model might not be able to fulfill its intended goal. The empirical likelihood is a distribution-free likelihood approach. The adjusted empirical likelihood functions exhibit many properties of the parametric likelihood functions and the AEL* (Liu and Chen, 2010) has been shown to outperform its predecessor. The robust likelihood introduced by Royall and Tsou (2003) is a parametric and yet robust approach. Our study shows that the parametric robust likelihood approach might be a better choice than AEL*.
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Authors who are presenting talks have a * after their name.
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