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Activity Number: 401
Type: Contributed
Date/Time: Tuesday, July 31, 2012 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Computing
Abstract - #305742
Title: A Robust Tobit Regression Model When Errors Are from the Epsilon Skew Exponential Power Family
Author(s): Jose Hector Guardiola*+ and Ahmad Flaih and Hassan Elsalloukh
Companies: Texas A&M University at Corpus Christi and University of Arkansas at Little Rock and University of Arkansas at Little Rock
Address: 6300 Ocean Drive, Corpus Christi, TX, 78412, United States
Keywords: Epsilon Skew Exponential Power (ESEP) family ; Tobit regression ; maximum likelihood estimation

In this paper, we generalize the Epsilon Skew Normal (ESN) Tobit regression model, proposed by Mashtare Jr. and Huston (2011), to the Epsilon Skew Exponential Power (ESEP) Tobit regression, which was proposed by Elsalloukh (2004). Tobit model assumes the normality of residuals term. Elsalloukh et al.(2005) proposed the Epsilon Skew Exponential Power (ESEP) family of distributions which includes the ESN distribution and many others distributions as special cases. This flexible family of distributions can accommodate both heavy tails and skewness behaviors. Therefore, ESEP can be considered as a "robust model" to cope with the deviation from normality. We propose the use of the Epsilon Skew Exponential Power family of distribution as an alternative model to make inference on estimating the interested parameters of the Tobit regression model. In the process, we develop the basic properties of the ESEP Tobit model, such as the structural equation, the expected value of the censored variable, and the loglikelihood functions based on the piecewise nature of the ESEP density.

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