Examinando por Autor "Lillo R.E."
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Ítem Author Correction: Unsupervised Scalable Statistical Method for Identifying Influential Users in Online Social Networks (Scientific Reports, (2018), 8, 1, (6955), 10.1038/s41598-018-24874-2)(Nature Publishing Group, 2019-01-01) Azcorra A.; Chiroque L.F.; Cuevas R.; Anta A.F.; Laniado H.; Lillo R.E.; Romo J.; Sguera C.; Universidad EAFIT. Escuela de Ciencias; Modelado MatemáticoThe original version of this Article contained an error in Affiliation 3, which was incorrectly given as ‘Department of Mathematical Sciences, Universidad EAFIT, Universidad Nacional de Colombia, Medellín, Colombia’. The correct affiliation is listed below: Department of Mathematical Sciences, Universidad EAFIT, Medellín, Colombia This error has now been corrected in the HTML and PDF versions of the Article and in the accompanying Supplementary Material file. © 2019, The Author(s).Ítem 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 MatemáticoA 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.Ítem On the estimation of extreme directional multivariate quantiles(Marcel Dekker Inc., 2019-01-01) Torres R.; Di Bernardino E.; Laniado H.; Lillo R.E.; Universidad EAFIT. Escuela de Ciencias; Modelado MatemáticoIn multivariate extreme value theory (MEVT), the focus is on analysis outside of the observable sampling zone, which implies that the region of interest is associated to high risk levels. This work provides tools to include directional notions into the MEVT, giving the opportunity to characterize the recently introduced directional multivariate quantiles (DMQ) at high levels. Then, an out-sample estimation method for these quantiles is given. A bootstrap procedure carries out the estimation of the tuning parameter in this multivariate framework and helps with the estimation of the DMQ. Asymptotic normality for the proposed estimator is provided and the methodology is illustrated with simulated data-sets. Finally, a real-life application to a financial case is also performed. © 2019, © 2019 Taylor & Francis Group, LLC.Ítem 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 MatemáticoA 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.Ítem 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.Ítem Unsupervised Scalable Statistical Method for Identifying Influential Users in Online Social Networks(Nature Publishing Group, 2018-05-03) Azcorra A.; Chiroque L.F.; Cuevas R.; Fernández Anta A.; Laniado H.; Lillo R.E.; Romo J.; Sguera C.; Universidad EAFIT. Escuela de Ciencias; Modelado MatemáticoBillions of users interact intensively every day via Online Social Networks (OSNs) such as Facebook, Twitter, or Google+. This makes OSNs an invaluable source of information, and channel of actuation, for sectors like advertising, marketing, or politics. To get the most of OSNs, analysts need to identify influential users that can be leveraged for promoting products, distributing messages, or improving the image of companies. In this report we propose a new unsupervised method, Massive Unsupervised Outlier Detection (MUOD), based on outliers detection, for providing support in the identification of influential users. MUOD is scalable, and can hence be used in large OSNs. Moreover, it labels the outliers as of shape, magnitude, or amplitude, depending of their features. This allows classifying the outlier users in multiple different classes, which are likely to include different types of influential users. Applying MUOD to a subset of roughly 400 million Google+ users, it has allowed identifying and discriminating automatically sets of outlier users, which present features associated to different definitions of influential users, like capacity to attract engagement, capacity to attract a large number of followers, or high infection capacity. © 2018 The Author(s).