Redução da causalidade espúria em métodos paramétricos de detecção de causalidade

• Ano de defesa: 2016
• Autor(a) principal: Vasconcelos, Vinicius Belmuds
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Espírito Santo; BR; Mestrado em Engenharia Elétrica; Centro Tecnológico; UFES; Programa de Pós-Graduação em Engenharia Elétrica
• Palavras-chave em Português: Causalidade de Granger; Correção de Bonferroni; Análise de causa e efeito; Causalidade; Análise de variância; Testes de hipóteses estatísticas; Estatística; Engenharia Elétrica; 621.3
• Link de acesso: http://repositorio.ufes.br/handle/10/9552

Methods for detection of the cause and effect relationships between variables have been developed and are applied in many areas of knowledge. Parametric and nonparametric methods require statistical significance tests to confirm or not the existence of causality. This work focuses on the parametric methods, in which a decision that affects the tests of statistical significance is the model order. The selection of the order is made here by different criteria. Granger methods and causal relationships based on residual analysis have their performance assessed under different criteria for choosing the order of models and greater rigor in the use of statistical tests of multiple comparisons. The goal is to reduce the number of spurious causal relationships, which can produce erroneous conclusions due to errors in the existing topology between the analyzed variables. The analysis and proposed improvements are evaluated using three case studies, one with synthetic data and two with data from different industrial processes.