Examinando por Autor "Cabana E."

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    Multivariate outlier detection based on a robust Mahalanobis distance with shrinkage estimators
    (Springer Verlag, 2019-01-01) Cabana E.; Lillo R.E.; Laniado H.; Universidad EAFIT. Escuela de Ciencias; Modelado Matemático
    A collection of robust Mahalanobis distances for multivariate outlier detection is proposed, based on the notion of shrinkage. Robust intensity and scaling factors are optimally estimated to define the shrinkage. Some properties are investigated, such as affine equivariance and breakdown value. The performance of the proposal is illustrated through the comparison to other techniques from the literature, in a simulation study and with a real dataset. The behavior when the underlying distribution is heavy-tailed or skewed, shows the appropriateness of the method when we deviate from the common assumption of normality. The resulting high true positive rates and low false positive rates in the vast majority of cases, as well as the significantly smaller computation time show the advantages of our proposal. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.
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    Robust regression based on shrinkage with application to Living Environment Deprivation
    (Springer, 2020-01-01) Cabana E.; Lillo R.E.; Laniado H.; Universidad EAFIT. Escuela de Ciencias; Modelado Matemático
    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 time, the affine equivariance and breakdown value of the regression estimator. Two classical data-sets often used in the literature and a real socioeconomic 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. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.
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    Robust regression based on shrinkage with application to Living Environment Deprivation
    (Springer, 2020-01-01) Cabana E.; Lillo R.E.; Laniado H.; Universidad EAFIT. Departamento de Ingeniería Mecánica; Estudios en Mantenimiento (GEMI)
    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 time, the affine equivariance and breakdown value of the regression estimator. Two classical data-sets often used in the literature and a real socioeconomic 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. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.