Estimação em regressão espacial inversa

Detalhes bibliográficos
Ano de defesa: 2015
Autor(a) principal: Cordeiro, Liliane Lopes
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Tese
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: UNIVERSIDADE FEDERAL DE LAVRAS
DEX - Programa de Pós-graduação
UFLA
BRASIL
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://repositorio.ufla.br/jspui/handle/1/9691
Resumo: In some issues involving regression analysis, it can be interesting to obtain estimates for a value of the independent variable, given a value of the dependent variable. This issue is determined inverse regression or calibration. In literature, there are two more commonly used methods for performing the point estimation in reverse regression models: classic and inverse. Methods to obtain interval estimations for the true value of the independent variable are also available. The main objective of this dissertation is to present the issue of spatial calibration and propose methods for the point and interval estimation in models that consider the spatial dependence structure between neighboring areas. The issue can be divided into two cases: in the first case, we intend to estimate the value of the independent variable belonging to the observed sample, while in the second case, the value of the independent variable to be estimated does not belong to the observed sample. This dissertation develops point and interval estimators for the value of the independent variable for the spatial autoregressive model (SAR). The estimators obtained are applied to real spatial data. The results obtained show the potential of inverse regression for issues in which the information from one region are directly influenced by the information from neighboring regions.