Victor España - Academia.edu (original) (raw)
Soy un profesional con una sólida formación académica respaldada por un Máster en Economía Empresarial de la Universidad de Neuchâtel, Suiza.Mi carrera abarca más de 40 años de experiencia en el mundo empresarial, durante los cuales he desempeñado roles clave como asesor y formador.Mi experiencia se ha consolidado en diversas empresas de renombre, donde he dejado mi huella como un experto en múltiples sectores.Mi trayectoria incluye:1. Bienes Raíces:– He asesorado y participado en operaciones inmobiliarias, brindando estrategias efectivas para la adquisición y gestión de propiedades.2. Marketing:– Me especializo en estrategias de marketing, ayudando a empresas a destacarse en entornos competitivos y a desarrollar campañas efectivas para llegar a su audiencia.3. Estrategias de Ventas:– Mi experiencia en estrategias de ventas se extiende a la formación de equipos comerciales, proporcionando las herramientas necesarias para alcanzar y superar objetivos de ventas.4. Criptomonedas:– Con un profundo conocimiento en el mundo de las criptomonedas, he asesorado sobre inversiones seguras y estrategias para aprovechar las oportunidades en este mercado emergente.
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Università degli Studi di Firenze (University of Florence)
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Papers by Victor España
The R Journal
eat is a new package for R that includes functions to estimate production frontiers and technical... more eat is a new package for R that includes functions to estimate production frontiers and technical efficiency measures through non-parametric techniques based upon regression trees. The package specifically implements the main algorithms associated with a recently introduced methodology for estimating the efficiency of a set of decision-making units in Economics and Engineering through Machine Learning techniques, called Efficiency Analysis Trees (Esteve et al. 2020). The package includes code for estimating input-and output-oriented radial measures, input-and output-oriented Russell measures, the directional distance function and the weighted additive model, plotting graphical representations of the production frontier by tree structures, and determining rankings of importance of input variables in the analysis. Additionally, it includes the code to perform an adaptation of Random Forest in estimating technical efficiency. This paper describes the methodology and implementation of the functions, and reports numerical results using a real data base application.
The R Journal
eat is a new package for R that includes functions to estimate production frontiers and technical... more eat is a new package for R that includes functions to estimate production frontiers and technical efficiency measures through non-parametric techniques based upon regression trees. The package specifically implements the main algorithms associated with a recently introduced methodology for estimating the efficiency of a set of decision-making units in Economics and Engineering through Machine Learning techniques, called Efficiency Analysis Trees (Esteve et al. 2020). The package includes code for estimating input-and output-oriented radial measures, input-and output-oriented Russell measures, the directional distance function and the weighted additive model, plotting graphical representations of the production frontier by tree structures, and determining rankings of importance of input variables in the analysis. Additionally, it includes the code to perform an adaptation of Random Forest in estimating technical efficiency. This paper describes the methodology and implementation of the functions, and reports numerical results using a real data base application.