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ARRUDA, H. A. An Extension of Nearest Neighbors Prediction Method for Mixed Poisson Model. 2017. Dissertação (Mestrado) Instituto de Matemática e Estatística, Universidade de São Paulo, São Paulo, 2017. Many proposals have been created in the last years for problems in the prediction of future observations in mixed models, however, there are few studies for cases that is necessary to assign random effects values for new groups. Tamura, Giampaoli and Noma (2013) proposed a method that computes the distances between a new group and groups with known random effects based on the values of the covariates, named as Nearest Neighbors Prediction Method (NNPM), considering the mixed logistic model. The goal of this dissertation was to extend the NNPM for the mixed Poisson model, in addition to obtaining confidence intervals for predictions. To attain such purposes new prediction performance measures were proposed as well as the use of Bootstrap methodology for the creation of intervals. The prediction method was applied in two sets of real data and in the simulation studies framework. In both cases good performances were obtained. Thus, the NNPM proved to be a viable prediction method also in the mixed Poisson case.

Extensão do Método de Predição do Vizinho mais Próximo para o modelo Poisson misto