DelaTorre-Diaz, Lorena
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DelaTorre-Diaz, Lorena
Official Name
De la Torre Díaz, Lorena
Alternative Name
lotorre
Torre-Diaz, Lorena de la
Main Affiliation
Scopus Author ID
57290222000

Research Output
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An Asset Index Proposal for Households in Mexico Applying the Mixed Principal Components Analysis Methodology

2021 , Rodríguez Aguilar, Román , DelaTorre-Diaz, Lorena

The development of assets indices has grown as an alternative to measure wealth from different generations in the evaluation of social mobility. A proposal of the development of an asset index is presented using the GSVD-based mixed principal components analysis (PCAMix package in R). The contribution rests in the combination of both numerical and categorical data and the integration of the simultaneous effect of these variables in the index. It was used in profiling the Mexican households according to the information from the 2018 National Household Income and Expenditure and the determination of the Gini coefficient to evaluate the inequality of distribution at the state level. Results show a high level of disparity in the distribution of assets with only 0.01% of the households possessing 40% or more of the assets included in the index, being the southern region where greatest challenges for ascending social mobility.© Springer Nature

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Critical Factors in the Participation of Women in Science, Technology, Engineering, and Mathematics -STEM- Disciplines in Mexico

2024-01-01 , Martínez Velasco, Antonieta Teodora , González, Fernando José Menéndez , DelaTorre-Diaz, Lorena , Terán-Bustamante, Antonia

Currently, women participate in STEM areas, still with a very marked gender gap. Taking this as a reference, in this work, an investigation has been carried out based on questionnaires applied to students of STEM careers. The information obtained was analyzed using multi-criteria decision methods. In particular, the Order of Preference by Similarity to the Ideal Solution (TOPSIS) method was applied to determine the most favorable conditions for women to study a STEM career. Through this analysis, this research has found that women's choice of a STEM career is strongly influenced firstly by the father's profession, secondly by the mother's profession, and also has a positive impact on the discrimination to which the person has been subjected, self-motivation. And self-esteem. These results indicate that it is necessary to influence the early educational stages to provide support from the family and school environment to women so that they develop their skills around STEM careers. In future work, the data obtained could be analyzed in greater depth, considering that the richness of the open responses may be lost by coding the respondents’ opinions as categorical variables. ©Springer.

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Crédito hipotecario: un modelo predictivo de discriminación de riesgo

2023 , González-Rossano, Carlos , DelaTorre-Diaz, Lorena , Terán-Bustamante, Antonia

Diversos estudios demuestran la relación entre el acceso a la vivienda y la superación de la pobreza. Sin embargo, existe un rezago en el acceso a la vivienda digna en México y la falta de historial crediticio es una limitante para el acceso a créditos bancarios. El objetivo de la presente investigación es analizar los criterios de selección de crédito hipotecario y proponer un modelo de gestión de riesgos que permita a la banca financiar a un mayor número de personas en la adquisición o mejora de su vivienda. La estrategia metodológica se basa en técnicas de aprendizaje automático apoyadas en la ciencia de datos para crear un modelo predictivo del cumplimiento del crédito basado en características individuales. Los resultados muestran un modelo predictivo de discriminación de riesgo con una confiabilidad del 85% para créditos a la vivienda, lo cual permite ampliar la base potencial de personas susceptibles de acceder a financiamiento hipotecario. El derecho a una vivienda digna presenta un rezago importante en el país y hasta ahora los bancos al proponer un modelo predictivo de selección de riesgo hipotecario se da respuesta a la pregunta de investigación que refiere a las acciones que puede ejecutar la banca para resolver el problema de falta de acceso a vivienda digna. Los bancos pueden establecer sus criterios de selección de riesgo apoyados en la ciencia y analítica de datos y la aplicación de modelos predictivos de aprendizaje automático utilizando su amplia base de datos histórica.© Revista Venezolana de Gerencia

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