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La producción de investigación en las universidades privadas: estudio de un caso
Gregorutti,Gustavo;
Enfoques , 2011,
Abstract: this case study describes how tecnológico de monterrey, in northern mexico, has experienced a qualitative and quantitative increase in its intellectual productivity. in less than ten years, this university has moved from generating few ideas to being one of most distinguished mexican private universities in the production of knowledge, patents and several research-derived businesses. this work explores how the re-elaboration of the institutional mission and the implementation of a model of research classes, among others, were decisive factors to increase the production of ideas. in this way, the university has increased its visibility and international ranking, attracting qualified researchers, developing students as future scientists. this study might be useful to administrator who are looking for ways of accomplishing the third mission in the 21st century
La acreditación de los posgrados en instituciones privadas de educación superior mexicanas
Gustavo Gregorutti
Reencuentro , 2010,
Abstract: El exponencial crecimiento de las instituciones privadas de educación superior en México en los últimos 30 a os también ha generado una serie de cuestionamientos sobre la calidad que éstas tienen en sus propuestas educativas de posgrado. Para saber cómo las instituciones privadas encaran el problema de la calidad, este trabajo recoge opiniones de cuatro administradores académicos entrevistados en tres universidades privadas del Estado de Nuevo León. Las opiniones expresadas transmiten preocupaciones acerca de los parámetros usados por Conacyt para evaluar la calidad en los programas profesionalizantes. Las mayores áreas de conflicto se evidenciaron en los modos de producción de conocimiento y los requerimientos tanto a docentes como a alumnos para formar parte de la acreditación. Se proveen alternativas y se discuten algunas presuposiciones del modelo imperante para evaluar la calidad.
La Universidad Confesional y los Nuevos Modelos de Universidades: Es Posible Mantener la Identidad?
Gustavo Gregorutti
Apuntes Universitarios , 2012,
Abstract: This study presents the dynamic relationship between the different mission statements and their impact on university’s models. The excessive promotion of the production of knowledge, as the central paradigm for higher education, has produced unbalances in universities. Religious affiliated institutions needs to repositionthemself with their characteristics to offering an education that includes all three existing missions, but adds a fourth one as a way of enriching training. This new mission purpose that centers on the development of wisdom, it is not only useful for religious affiliated universities, but also for all kind of higher education models. Toward the end, the paper turns to some of the complications a more balanced model would face.
Grouped variable importance with random forests and application to multiple functional data analysis
Baptiste Gregorutti,Bertrand Michel,Philippe Saint-Pierre
Statistics , 2014, DOI: 10.1016/j.csda.2015.04.002
Abstract: The selection of grouped variables using the random forest algorithm is considered. First a new importance measure adapted for groups of variables is proposed. Theoretical insights into this criterion are given for additive regression models. Second, an original method for selecting functional variables based on the grouped variable importance measure is developed. Using a wavelet basis, it is proposed to regroup all of the wavelet coefficients for a given functional variable and use a wrapper selection algorithm with these groups. Various other groupings which take advantage of the frequency and time localization of the wavelet basis are proposed. An extensive simulation study is performed to illustrate the use of the grouped importance measure in this context. The method is applied to a real life problem coming from aviation safety.
Correlation and variable importance in random forests
Baptiste Gregorutti,Bertrand Michel,Philippe Saint-Pierre
Statistics , 2013,
Abstract: This paper is about variable selection with the random forests algorithm in presence of correlated predictors. In high-dimensional regression or classification frameworks, variable selection is a difficult task, that becomes even more challenging in the presence of highly correlated predictors. Firstly we provide a theoretical study of the permutation importance measure for an additive regression model. This allows us to describe how the correlation between predictors impacts the permutation importance. Our results motivate the use of the Recursive Feature Elimination (RFE) algorithm for variable selection in this context. This algorithm recursively eliminates the variables using permutation importance measure as a ranking criterion. Next various simulation experiments illustrate the efficiency of the RFE algorithm for selecting a small number of variables together with a good prediction error. Finally, this selection algorithm is tested on the Landsat Satellite data from the UCI Machine Learning Repository.
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