Examinando por Autor "Laniado, H."
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Ítem Big data?: historia, definición, herramientas y aplicaciones en la industria(2019-01-07) Toro, M.; Laniado, H.; Universidad EAFIT. Escuela de Ciencias; Modelado MatemáticoDan Ariely, profesor de psicología y economía de la Universidad de Duke, plantea que "El big data [en español, datos masivos] es como el sexo en la adolescencia: todo el mundo habla de ello, nadie sabe realmente cómo hacerlo, todos piensan que los demásÍtem Big data?: historia, definición, herramientas y aplicaciones en la industria(2019-01-07) Toro, M.; Laniado, H.; Toro, M.; Laniado, H.; Universidad EAFIT. Departamento de Ingeniería de Sistemas; I+D+I en Tecnologías de la Información y las ComunicacionesDan Ariely, profesor de psicología y economía de la Universidad de Duke, plantea que "El big data [en español, datos masivos] es como el sexo en la adolescencia: todo el mundo habla de ello, nadie sabe realmente cómo hacerlo, todos piensan que los demásÍtem Development of a robust customer satisfaction index for domestic air journeys(Elsevier Ltd, 2020-01-01) Munoz, C.; Laniado, H.; Córdoba, J.; Universidad EAFIT. Escuela de Ciencias; Modelado MatemáticoThis research proposes a Robust Customer Satisfaction Index for air domestic journeys (RCSI), which could be less sensitive to outlier data than index scores based on the American customer satisfaction index (ACSI) formulation. Since traveler experiences in air journeys are a chain of services related to departure airport service, airline service, and arrival airport service, a new index for measuring passenger satisfaction for air journeys is required. In a sense then, this study is the first step towards integrating satisfaction literature to propose a robust index for air journeys. The Structural Equation Model (SEM) was used to validate the theoretical model. The RCSI model was tested in the context of Colombian major domestic air-route where traveler's perceived quality and perceived value were found to predict significantly overall passenger satisfaction. In this study, we found that the RCSI score is similar to the average for the airline industry in ACSI. The findings show that the RCSI is less sensitive to outlier data than customer satisfaction indexes (CSIs) based on the ACSI model formulation. The RCSI model also allows the airline and airport managers to understand the specific factors, which significantly influence overall traveler satisfaction, by reading the causal relationship in the RCSI model. © 2020 Elsevier LtdÍtem Development of a robust customer satisfaction index for domestic air journeys(Elsevier Ltd, 2020-01-01) Munoz, C.; Laniado, H.; Córdoba, J.; Universidad EAFIT. Departamento de Ingeniería Mecánica; Estudios en Mantenimiento (GEMI)This research proposes a Robust Customer Satisfaction Index for air domestic journeys (RCSI), which could be less sensitive to outlier data than index scores based on the American customer satisfaction index (ACSI) formulation. Since traveler experiences in air journeys are a chain of services related to departure airport service, airline service, and arrival airport service, a new index for measuring passenger satisfaction for air journeys is required. In a sense then, this study is the first step towards integrating satisfaction literature to propose a robust index for air journeys. The Structural Equation Model (SEM) was used to validate the theoretical model. The RCSI model was tested in the context of Colombian major domestic air-route where traveler's perceived quality and perceived value were found to predict significantly overall passenger satisfaction. In this study, we found that the RCSI score is similar to the average for the airline industry in ACSI. The findings show that the RCSI is less sensitive to outlier data than customer satisfaction indexes (CSIs) based on the ACSI model formulation. The RCSI model also allows the airline and airport managers to understand the specific factors, which significantly influence overall traveler satisfaction, by reading the causal relationship in the RCSI model. © 2020 Elsevier LtdÍtem Directional multivariate extremes in environmental phenomena(John Wiley and Sons Ltd, 2017-03-01) Torres, R.; De michele, C.; Laniado, H.; Lillo, R.E.; Universidad EAFIT. Escuela de Ciencias; Modelado MatemáticoSeveral environmental phenomena can be described by different correlated variables that must be considered jointly in order to be more representative of the nature of these phenomena. For such events, identification of extremes is inappropriate if it is based on marginal analysis. Extremes have usually been linked to the notion of quantile, which is an important tool to analyze risk in the univariate setting. We propose to identify multivariate extremes and analyze environmental phenomena in terms of the directional multivariate quantile, which allows us to analyze the data considering all the variables implied in the phenomena, as well as look at the data in interesting directions that can better describe an environmental catastrophe. Because there are many references in the literature that propose extremes detection based on copula models, we also generalize the copula method by introducing the directional approach. Advantages and disadvantages of the nonparametric proposal that we introduce and the copula methods are provided in the paper. We show with simulated and real data sets how by considering the first principal component direction we can improve the visualization of extremes. Finally, two cases of study are analyzed: a synthetic case of flood risk at a dam (a three-variable case) and a real case study of sea storms (a five-variable case). Copyright © 2017 John Wiley & Sons, Ltd.Ítem A directional multivariate value at risk(Elsevier, 2015-11-01) Torres, R.; Lillo, R.E.; Laniado, H.; Universidad EAFIT. Escuela de Ciencias; Modelado MatemáticoIn economics, insurance and finance, value at risk (VaR) is a widely used measure of the risk of loss on a specific portfolio of financial assets. For a given portfolio, time horizon, and probability alpha, the 100 alpha% VaR is defined as a threshold loss value, such that the probability that the loss on the portfolio over the given time horizon exceeds this value is alpha. That is to say, it is a quantile of the distribution of the losses, which has both good analytic properties and easy interpretation as a risk measure. However, its extension to the multivariate framework is not unique because a unique definition of multivariate quantile does not exist. In the current literature, the multivariate quantiles are related to a specific partial order considered in R-n, or to a property of the univariate quantile that is desirable to be extended to R-n. In this work, we introduce a multivariate value at risk as a vector-valued directional risk measure, based on a directional multivariate quantile, which has recently been introduced in the literature. The directional approach allows the manager to consider external information or risk preferences in her/his analysis. We derive some properties of the risk measure and we compare the univariate VaR over the marginals with the components of the directional multivariate VaR. We also analyze the relationship between some families of copulas, for which it is possible to obtain closed forms of the multivariate VaR that we propose. Finally, comparisons with other alternative multivariate VaR given in the literature, are provided in terms of robustness. (C) 2015 Elsevier B.V. All rights reserved.Ítem Modeling Air Travelers' Experience Based on Service Quality Stages Related to Airlines and Airports(2019-10-12) Department of Civil Engineering, Universidad Nacional de Colombia, Medellín, Col; Laniado, H.; Department of Civil Engineering, Universidad Nacional de Colombia, Medellín, Col; Universidad EAFIT. Escuela de Ciencias; Modelado MatemáticoÍtem Modeling Air Travelers' Experience Based on Service Quality Stages Related to Airlines and Airports(2019-10-12) Department of Civil Engineering, Universidad Nacional de Colombia, Medellín, Col; Laniado, H.; Department of Civil Engineering, Universidad Nacional de Colombia, Medellín, Col; Universidad EAFIT. Departamento de Ingeniería Mecánica; Estudios en Mantenimiento (GEMI)Ítem Robust Three-Step Regression Based on Comedian and Its Performance in Cell-Wise and Case-Wise Outliers(MDPI AG, 2020-08-01) Laniado, H.; Toro, M.; Universidad EAFIT. Escuela de Ciencias; Modelado MatemáticoÍtem Robust Three-Step Regression Based on Comedian and Its Performance in Cell-Wise and Case-Wise Outliers(MDPI AG, 2020-08-01) Laniado, H.; Toro, M.; Laniado, H.; Toro, M.; Universidad EAFIT. Departamento de Ingeniería de Sistemas; I+D+I en Tecnologías de la Información y las ComunicacionesÍtem Robust Three-Step Regression Based on Comedian and Its Performance in Cell-Wise and Case-Wise Outliers(MDPI AG, 2020-08-01) Laniado, H.; Toro, M.; Universidad EAFIT. Departamento de Ingeniería Mecánica; Estudios en Mantenimiento (GEMI)