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Developing ridge estimation method for median regression

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  • Zangin Zeebari

Abstract

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.

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  • Zangin Zeebari, 2012. "Developing ridge estimation method for median regression," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(12), pages 2627-2638, August.
    • Handle: RePEc:taf:japsta:v:39:y:2012:i:12:p:2627-2638
    DOI: 10.1080/02664763.2012.724663
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    References listed on IDEAS

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    1. Moshe Buchinsky, 1998. "Recent Advances in Quantile Regression Models: A Practical Guideline for Empirical Research," Journal of Human Resources, University of Wisconsin Press, vol. 33(1), pages 88-126.
    2. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731, September.
    3. Wang, Hansheng & Li, Guodong & Jiang, Guohua, 2007. "Robust Regression Shrinkage and Consistent Variable Selection Through the LAD-Lasso," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 347-355, July.
    4. Koenker, Roger & Bassett, Gilbert, Jr, 1982. "Robust Tests for Heteroscedasticity Based on Regression Quantiles," Econometrica, Econometric Society, vol. 50(1), pages 43-61, January.
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    Cited by:

    1. Andrey A. Gnidchenko & Vladimir A. Salnikov, 2015. "Net Comparative Advantage Index: Overcoming the Drawbacks of the Existing Indices," HSE Working papers WP BRP 119/EC/2015, National Research University Higher School of Economics.

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