Uma abordagem estatística para a análise dos resultados das eleições presidenciais

• Ano de defesa: 2024
• Autor(a) principal: Lachos Olivares, Victor Eduardo
• Orientador(a): Bazán Guzmán, Jorge Luis
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de São Carlos; Câmpus São Carlos
• Programa de Pós-Graduação: Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs
• Palavras-chave em Português: Análise de componentes principais; Transformação de razão logarítmica; Dados composicionais; Escores; Cargas; Modelo de regressão Beta; Cópulas
• Palavras-chave em Inglês: Principal component analysis; Log-ratio transformation; Compositional data; Scores; Loadings; Beta regression Model; Copulas
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA::ESTATISTICA::ANALISE DE DADOS; CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA::ESTATISTICA::ANALISE MULTIVARIADA; CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA::ESTATISTICA::INFERENCIA PARAMETRICA; CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA::ESTATISTICA::REGRESSAO E CORRELACAO
• Link de acesso: https://repositorio.ufscar.br/handle/ufscar/19547

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.