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Abstract Details
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Activity Number:
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655
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Type:
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Contributed
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Date/Time:
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Thursday, August 4, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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| Abstract - #301098 |
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Title:
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A Regression Model When the Errors Are Asymmetric and Heavy Tailed Distributed
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Author(s):
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Howraa Al-Mousawi*+ and Hassan Elsalloukh
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Companies:
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University of Arkansas at Little Rock and University of Arkansas at Little Rock
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Address:
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2801 South University Ave., Little Rock, AR, 72204-1099,
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Keywords:
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Asymmetric Skew distribution ;
Epsilon skew Laplace distribution ;
Heavy tail distribution ;
Simple linear regression
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Abstract:
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One of the most important problems that arises in fitting a regression model is when the errors are not normally distributed or have a heavy tailed distribution. The heavy tailed distribution allows for higher probability of extreme observations. In this work, we develop new flexible regression models using the Epsilon Skew Laplace distribution (ESL) defined in Elsalloukh (2005, 2008), to model skewed and heavy tailed data. In the process, we provide alternative estimators for the model parameters to the Least Square Estimators (LS) and give some other properties. Finally, we estimate the Maximum Likelihood Estimators (MLE) of the regression parameters.
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