Modelos espaciais com distribuição normal independente

Detalhes bibliográficos
Ano de defesa: 2017
Autor(a) principal: Douglas Mateus da Silva
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Universidade Federal de Minas Gerais
UFMG
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Link de acesso: http://hdl.handle.net/1843/ICED-ANLQVK
Resumo: Linear spacial models, known as geostatistics models, are used in the study of phenomena that are spatially correlated. Although it is usual to admit that the spacial process follows a Gaussian distribution, the presence of atypical observations can make this analysis inappropriate. Thus, it might be necessary to consider symmetrical distributions with heavier tails. In this work, we studied the linear spatial model with normal/independent distribution, which is an alternative to the Gaussian spacial model. In this class, more robust distributions are included, such as t-Student and slash. We developed estimation methods by maximizing the likelihood function and EM algorithm. We also performed simulations under different scenarios to evaluate the performance of the algorithms. The construction of thematic maps was performed using the kriging technique adapted to the proposed model. Using the local influence method proposed by Cook (1986), we show a diagnostic study of the linear spacial model with normal/independent distribution. We conclude with an illustration of the proposed methodology with a data set of daily precipitation in Switzerland after the Chernobyl disaster occurred in 1986.