PAULO CILAS MARQUES FILHO
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Artigo Científico Predictive Analysis of Microarray Data(2014) PAULO CILAS MARQUES FILHO; Pereira, Carlos A. de B.Artigo Científico Bayesian generalizations of the integer-valued autoregressive model(2020) PAULO CILAS MARQUES FILHO; Graziadei, Helton; HEDIBERT FREITAS LOPESWe develop two Bayesian generalizations of the Poisson integer-valued autoregressive model. The AdINAR(1) model accounts for overdispersed data by means of an innovation process whose marginal distributions are finite mixtures, while the DP-INAR(1) model is a hierarchical extension involving a Dirichlet process, which is capable of modeling a latent pattern of heterogeneity in the distribution of the innovations rates. The probabilistic forecasting capabilities of both models are put to test in the analysis of crime data in Pittsburgh, with favorable results.Trabalho de Conclusão de Curso The Illusion of the Illusion of Sparsity: the Effects of Using a Wrong Prior(2019) Fava, Bruno Vinicius NunesThe emergence of Big Data raises the question of how to model statistical series when there is a big number of possible regressors. This monograph addresses the issue by comparing the possibility of using dense or sparse models in a Bayesian approach, allowing for variable selection and shrinkage. We discuss the results reached by Giannone, Lenza e Primiceri (2018) through a “Spike-and-Slab” prior, that suggest an “illusion of sparsity” in economic datasets, as no clear patterns of sparsity could be found. We make a further revision of the posterior distributions of the model, and propose three experiments to evaluate the robustness of the adopted prior distribution. We find that the model indirectly induces variable selection and shrinkage, what suggests that the “illusion of sparsity” is, itself, an illusionArtigo Científico Probabilistic Nearest Neighbors Classification(2024) Fava, Bruno; PAULO CILAS MARQUES FILHO; HEDIBERT FREITAS LOPESAnalysis of the currently established Bayesian nearest neighbors classification model points to a connection between the computation of its normalizing constant and issues of NP-completeness. An alternative predictive model constructed by aggregating the predictive distributions of simpler nonlocal models is proposed, and analytic expressions for the normalizing constants of these nonlocal models are derived, ensuring polynomial time computation without approximations. Experiments with synthetic and real datasets showcase the predictive performance of the proposed predictive model.Artigo Científico Prior Sensitivity Analysis in a Semi-Parametric Integer-Valued Time Series Model(2020) Graziadei, Helton; Lijoi, Antonio; HEDIBERT FREITAS LOPES; PAULO CILAS MARQUES FILHO; Prünster, IgorArtigo Científico Confidence intervals for the random forest generalization error(2022) PAULO CILAS MARQUES FILHOWe show that the byproducts of the standard training process of a random forest yield not only the well known and almost computationally free out-of-bag point estimate of the model generalization error, but also open a direct path to compute confidence intervals for the generalization error which avoids processes of data splitting and model retraining. Besides the low computational cost involved in their construction, these confidence intervals are shown through simulations to have good coverage and appropriate shrinking rate of their width in terms of the training sample size.Livro Data Science, Marketing & Business(2020) PAULO CILAS MARQUES FILHO; Fernandez, Pedro J.Artigo Científico Bayesian generalizations of the integer-valued autoregressive model(2022) HEDIBERT FREITAS LOPES; PAULO CILAS MARQUES FILHO; Graziadei, HeltonArtigo Científico Bayesian Analysis of Simple Random Densities(2014) PAULO CILAS MARQUES FILHO; Pereira, Carlos A. de B.