Examinando por Materia "Outliers"

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    Detecting Outliers with a Non-parametric estimation of the Mahalanobis distance
    (Universidad EAFIT, 2023) Piedrahita Jaramillo, Catalina; Laniado Rodas, Henry; Saldarriaga Aristizábal, Pablo Andrés
    This paper proposes the creation of a robust version of the Mahalanobis distance for the outlier’s identification problem, using robust and non-parametric estimations for the covariance matrix, such as Kendall’s Tau and Median Absolute Deviation (MAD), as well as techniques that enhance the numerical properties of the covariance matrix to reduce error during numerical calculations like Ledoit and Wolf’s Shrinkage. The performance of the methods is evaluated through simulation of independent normal data, correlated normal data, and real data sets and compared with some methods from the literature. The proposed methods achieve a high percentage of correct identification of outliers and have a low false positive rate for both data types, particularly in the case of correlated normal data.
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    Outliers in semi-parametric Estimation of Treatment Effects
    (Universidad EAFIT, 2017-10-30) Ugarte Ontiveros, Darwin; Canavire-Bacarreza, Gustavo; Castro Peñarrieta, Luis; gcanavir@eafit.edu.co
    Average treatment effects estimands can present significant bias under the presence of outliers. Moreover, outliers can be particularly hard to detect, creating bias and inconsistency in the semi-parametric ATE estimads. In this paper, we use Monte Carlo simulations to demonstrate that semi-parametric methods, such as matching, are biased in the presence of outliers. Bad and good leverage points outliers are considered. The bias arises because bad leverage points completely change the distribution of the metrics used to define counterfactuals. Whereas good leverage points increase the chance of breaking the common support condition and distort the balance of the covariates and which may push practitioners to misspecify the propensity score. We provide some clues to diagnose the presence of outliers and propose a reweighting estimator that is robust against outliers based on the Stahel-Donoho multivariate estimator of scale and location. An application of this estimator to LaLonde (1986) data allows us to explain the Dehejia and Wahba (2002) and Smith and Todd (2005) debate on the inability of matching estimators to deal with the evaluation problem.
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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.
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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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    Treatment of Outliers to Study Railway Vibrations Transmission
    (Universidad EAFIT, 2012-12-01) González–Cancelas, Nicolett; Soler-Flores, Francisco; Camarero Orive, Alberto; López Ansorena, Íñigo; Universidad Politécnica de Madrid