Shrinkage reweighted regression

Henry Laniado Rodas
Authors registered in the RePEc Author Service: Elisa Cabana Garceran del Vall

DES - Working Papers. Statistics and Econometrics. WS from Universidad Carlos III de Madrid. Departamento de Estadística

Abstract: A robust estimator is proposed for the parameters that characterize the linear regression problem. It is based on the notion of shrinkages, often used in Finance and previously studied for outlier detection in multivariate data. A thorough simulation study is conducted to investigate: the efficiency with normal and heavy-tailed errors, the robustness under contamination, the computational times, the affine equivariance and breakdown value of the regression estimator. Two classical data-sets often used in the literature and a real socio-economic data-set about the Living Environment Deprivation of areas in Liverpool (UK), are studied. The results from the simulations and the real data examples show the advantages of the proposed robust estimator in regression.

Keywords: Robust; Regression; Robust; Mahalanobis; Distance; Shrinkage; Estimator; Outliers; Environmental; Study (search for similar items in EconPapers)
Date: 2019-06
New Economics Papers: this item is included in nep-ecm and nep-ets
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Persistent link: https://EconPapers.repec.org/RePEc:cte:wsrepe:28500

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