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A Simulation Study on the Least Absolute Deviations Method for Ridge Regression
Högskolan i Jönköping, Internationella Handelshögskolan, IHH, Economics, Finance and Statistics.ORCID-id: 0000-0003-2733-4441
2012 (engelsk)Inngår i: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415XArtikkel i tidsskrift (Fagfellevurdert) Submitted
Abstract [en]

Though the Least Absolute Deviations (LAD) method of estimation is robust, there is still the possibility of having strong multicollinearity of the predictors in a linear regression analysis. The paper consists of the application of the LAD estimation instead of the Ordinary Least Squares (OLS) estimation for the ridge regression and a simulation study to assess the performance of some biasing parameters used in the literature with their new LAD versions. The aim is to deal with the cases when the predictors are highly collinear  and the error terms are asymmetric or heavy-tailed, by giving more room to the bias in order to reduce the Mean Squared Error (MSE) of the LAD estimators.

sted, utgiver, år, opplag, sider
Taylor & Francis, 2012.
Emneord [en]
Least Absolute Deviation, Ridge Regression, Robust Estimators
HSV kategori
Identifikatorer
URN: urn:nbn:se:hj:diva-17462OAI: oai:DiVA.org:hj-17462DiVA, id: diva2:485257
Tilgjengelig fra: 2012-08-30 Laget: 2012-01-28 Sist oppdatert: 2018-06-07bibliografisk kontrollert
Inngår i avhandling
1. On Median and Ridge Estimation of SURE Models
Åpne denne publikasjonen i ny fane eller vindu >>On Median and Ridge Estimation of SURE Models
2012 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Jönköping: Jönköping International Business School, 2012. s. 159
Serie
JIBS Dissertation Series, ISSN 1403-0470 ; 083
HSV kategori
Identifikatorer
urn:nbn:se:hj:diva-19681 (URN)978-91-86345-36-5 (ISBN)
Disputas
2012-11-16, B1014 at JIBS, Högskoleområdet Gjuterigatan 5, Jönköping, 10:00
Opponent
Veileder
Tilgjengelig fra: 2012-10-24 Laget: 2012-10-24 Sist oppdatert: 2018-06-07bibliografisk kontrollert

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