Utilizing the Flexibility of the Epsilon-Skew-Normal Distribution for Common Regression Problems

Alan Hutson

Journal of Applied Statistics, 2004, vol. 31, issue 6, 673-683

Abstract: In this paper we illustrate the properties of the epsilon-skew-normal (ESN) distribution with respect to developing more flexible regression models. The ESN model is a simple one-parameter extension of the standard normal model. The additional parameter ~ corresponds to the degree of skewness in the model. In the fitting process we take advantage of relatively new powerful routines that are now available in standard software packages such as SAS. It is illustrated that even if the true underlying error distribution is exactly normal there is no practical loss n power with respect to testing for non-zero regression coefficients. If the true underlying error distribution is slightly skewed, the ESN model is superior in terms of statistical power for tests about the regression coefficient. This model has good asymptotic properties for samples of size n>50.

Keywords: Robust Regression; Epsilon-skew-normal Distribution (search for similar items in EconPapers)
Date: 2004
References: Add references at CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.tandfonline.com/doi/abs/10.1080/1478881042000214659 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:31:y:2004:i:6:p:673-683

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CJAS20

DOI: 10.1080/1478881042000214659

Access Statistics for this article

Journal of Applied Statistics is currently edited by Robert Aykroyd

More articles in Journal of Applied Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().