Change search
ReferencesLink to record
Permanent link

Direct link
Developing ridge estimation method for median regression
Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
2012 (English)In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 39, no 12, 2627-2638 p.Article in journal (Refereed) Published
Abstract [en]

In this paper, the ridge estimation method is generalized to the median regression. Though the least absolute deviation (LAD) estimation method is robust in the presence of non-Gaussian or asymmetric error terms, it can still deteriorate into a severe multicollinearity problem when non-orthogonal explanatory variables are involved. The proposed method increases the efficiency of the LAD estimators by reducing the variance inflation and giving more room for the bias to get a smaller mean squared error of the LAD estimators. This paper includes an application of the new methodology and a simulation study as well.

Place, publisher, year, edition, pages
2012. Vol. 39, no 12, 2627-2638 p.
National Category
Probability Theory and Statistics
URN: urn:nbn:se:hj:diva-19676DOI: 10.1080/02664763.2012.724663OAI: diva2:562317
Available from: 2012-10-24 Created: 2012-10-24 Last updated: 2012-10-24Bibliographically approved
In thesis
1. On Median and Ridge Estimation of SURE Models
Open this publication in new window or tab >>On Median and Ridge Estimation of SURE Models
2012 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This doctoral dissertation is a progressive generalization of some of the robust estimation methods in order to make those methods applicable to the estimation of the Seemingly Unrelated Regression Equations (SURE) models. The robust methods are each of the Least Absolute Deviations (LAD) estimation method, also known as the median regression, and the ridge estimation method. The first part of the dissertation consists of a brief explanation of the LAD and the ridge methods. The contribution of this investigation to the statistical methodology is focused on in the second part of the dissertation, which consists of 5 articles.

The first article is a generalization of the median regression to the estimation of the SURE models. The proposed methodology is compared with each of the Generalized Least Squares (GLS) method and the median regression of individual regression equations.

The second article generalizes the median regression on the conventional multivariate regression analysis, i.e., the SURE models with the same design matrices of the equations. The results are compared with the median regression of individual regression equations and the conventionally used OLS estimation method for such models (which is equivalent to the GLS estimation, as well).

In the third article, the author develops ridge estimation for the median regression. Some properties and the asymptotic distribution of the estimator presented are investigated, as well. An empirical example is used to assess the performance of the new methodology.

In the fourth article, the properties of some biasing parameters used in the literature for ridge regression are investigated when they are used for the new methodology proposed in the third article.

In the last article, the methodologies of the four preceding articles are assembled in a more generalized methodology to develop the ridge-type estimation of the LAD method for the SURE models. This article has also provided an opportunity to investigate the behavior of some biasing parameters for the SURE models, which were previously used by some researchers in a non-SURE context.

Place, publisher, year, edition, pages
Jönköping: Jönköping International Business School, 2012. 159 p.
JIBS Dissertation Series, ISSN 1403-0470 ; 083
National Category
Economics and Business
urn:nbn:se:hj:diva-19681 (URN)978-91-86345-36-5 (ISBN)
Public defence
2012-11-16, B1014 at JIBS, Högskoleområdet Gjuterigatan 5, Jönköping, 10:00
Available from: 2012-10-24 Created: 2012-10-24 Last updated: 2012-10-24Bibliographically approved

Open Access in DiVA

fulltext(1568 kB)382 downloads
File information
File name FULLTEXT01.pdfFile size 1568 kBChecksum SHA-512
Type fulltextMimetype application/pdf

Other links

Publisher's full text

Search in DiVA

By author/editor
Zeebari, Zangin
By organisation
JIBS, Economics, Finance and Statistics
In the same journal
Journal of Applied Statistics
Probability Theory and Statistics

Search outside of DiVA

GoogleGoogle Scholar
Total: 382 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Altmetric score

Total: 139 hits
ReferencesLink to record
Permanent link

Direct link