Hierarquização geométrica dos preditores geoestatísticos

• Ano de defesa: 2010
• Autor(a) principal: Santos, Gérson Rodrigues dos
• Orientador(a): Oliveira, Marcelo Silva de
• Banca de defesa: Nogueira, Denismar Alves, Chaves, Lucas Monteiro, Ferreira, Eric Batista, Scalon, João Domingos
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: UNIVERSIDADE FEDERAL DE LAVRAS
• Programa de Pós-Graduação: DEX - Departamento de Ciências Exatas
• País: BRASIL
• Palavras-chave em Português: Geoestatística; Espaços de Hilbert; Esperança condicional; Krigagem; Hierarquização geométrica; Geostatistics; Hilbert spaces; Conditional expectation; Kriging; Geometric hierarchy
• Área do conhecimento CNPq: CNPQ_NÃO_INFORMADO
• Link de acesso: https://repositorio.ufla.br/handle/1/4304

Geostatistics estimation is a probabilistic modeling strategy that uses sample points neighborhood information in order to consider the spatial dependence of the structure of a STOCHASTICprocess to obtain, unbiased and minimum variance estimates. Hence, the main goal of this thesis is to present the geometric hierarchy of the geostatistical linear predictors, taking into account their restrictions and respective projection subspaces. We apply the orthogonal geometric projection of the predictors in order to obtain a vector function Y(x0), (that represents a value to be predicted in the location (x0)) in the resulting vector subspace from the restrictions imposed to the predictors. As an important result obtained in our work, we show how to obtain geometric hierarchy: Conditional expectation, simple linear kriging and linear universal kriging, 1st, 2nd and 3rd, respectively. We establish practical conditions for using such predictors, and we ilustrate our findings with stochastic simulations.