Directional data require specialized models because of the non-Euclidean nature of their
domain. When a directional variable is observed jointly with linear variables, modeling their …

Disaster risk evolves spatially and temporally due to the combined dynamics of hazards,
exposure and vulnerability. However, most previous risk assessments of natural disasters …

Abstract Numerical Weather Prediction (NWP) models are often used to predict
meteorological events in a deterministic way. In recent years, operational Ensemble …

In oceanography, modeling wave fields requires the use of statistical tools capable of
handling the circular nature of the data measurements. An important issue in ocean wave …

In psychological, social, behavioral, and medical studies, hidden Markov models (HMMs)
have been extensively applied to the simultaneous modeling of heterogeneous observation …

In this work, we introduce a model for circular data analysis to robustly estimate parameters,
under a longitudinal clustering setting. A hidden Markov model for longitudinal circular data …

Cluster analysis is widely used in data analysis. Statistical data analysis is generally
performed on the linear data. If the data has directional structure, classical statistical …

This article presents a Bayesian model for the clustering of non-ordered multivariate
directional or circular data. The particular trait of our data is that each single observation is …

Nos últimos anos, com o rápido desenvolvimento da geração de eletricidade por meio das
fontes eólica e solar, alguns problemas têm surgido gradativamente (incerteza e …

Regression estimation can be approached using nonparametric procedures, producing
exible estimators and avoiding misspeci cation problems. Alternatively, parametric methods …