Item Response Theory (IRT) models are the basis of modern educational measurement. In order to increase testing efficiency, modern tests make ample use of groups of questions associated with a single stimulus (testlets). This violates the IRT assumption of local independence. However, a set of...

The main objective of this PhD was to further develop Bayesian spatio-temporal models (specifically the Conditional Autoregressive (CAR) class of models), for the analysis of sparse disease outcomes such as birth defects. The motivation for the thesis arose from problems encountered when...

Wind power generation is essential to reduce the use of fossil resources and, consequently, the emission of greenhouse gases (GHG). Currently, the National Interconnected System (SIN) has about 9971 wind turbines in operation, the sixth largest network in the world ranking, and 80% of Brazilian...

Modeling real estate prices in the context of hedonic models often involves fitting a Generalized Additive Model, where only the mean of a (lognormal) distribution is regressed on a set of variables without taking other parameters of the distribution into account. Thus far, the application of...

This work presents a Bayesian approach for inferring the capacity factor of an offshore wind farm. The application of the proposed methodology is illustrated through a case study of a wind power offshore project located on the northern coast of the Rio de Janeiro State.

Models with structured additive predictor provide a very broad and rich framework for complex regression modeling. They can deal simultaneously with nonlinear covariate effects and time trends, unit- or cluster-specific heterogeneity, spatial heterogeneity and complex interactions between...