Disertación de Maestría
DOI
https://doi.org/10.11606/D.104.2024.tde-05032024-103928
Documento
Autor
Nombre completo
Victor Eduardo Lachos Olivares
Dirección Electrónica
Área de Conocimiento
Fecha de Defensa
Publicación
São Carlos, 2024
Director
Tribunal
Guzmán, Jorge Luis Bazán (Presidente)
Prates, Marcos Oliveira
Serrano, Luis Hilmar Valdivieso

Título en portugués
Uma Abordagem Estatística para a Análise dos Resultados das Eleições Presidenciais
Palabras clave en portugués
Análise de componentes principais
Modelo de regressão Beta
Transformação de razão logarítmica
Resumen en portugués

Título en inglés
An statistical approach for analysis of presidential elections results
Palabras clave en inglés
Beta regression model
Compositional data
Log-ratio transformation
Principal component analysis
Resumen en inglés
Multiparty data has characteristics that make it compositional data such as a constant sum of components and a limited space known as simplex. Thus, the purpose of the work is to develop a methodology to analyze multi-party data from electoral elections considering their restricted nature. In this context, the proposed methodology consists of 8 steps: initially, we collect multi-party data and transform it into compositional data. Then, we apply the log-ratio transformation , removing the inherent constraints of compositional data. Next, we employ principal component analysis (PCA) to reduce dimensionality and identify the principal components that retain most of the variation in the data. These components are analyzed based on two important metrics: loadings and scores. Given that the scores have different variability in the components, they are transformed between values of zero and one. Subsequently, we propose the Beta regression model considering the scores as the response variable, and the human development indicators as the explanatory variables. The methodology is applied to multiparty data from the first round elections in Peru in 2021 and Brazil in 2022, allowing us to identify the main components and which covariates (health, education and income) are directly related to votes in different regions and states. Finally, considering that data from presidential elections of Peru 2021 with two response variables, we propose a bivariate regression model via copulas and analyze the dependence structure between these variables.

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Fecha de Publicación
2024-03-05