The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Online Program Home
Abstract Details
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
|
Abstract:
|
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.
|
The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
Back to the full JSM 2012 program
|
2012 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Continuing Education program, please contact the Education Department.