A Rasch Poisson counts (RPC) model is described to identify individual latent traits and facilities of the items tests that error (or success) count in several tasks over time, instead modeling correct responses a test as dichotomous item response theory (IRT) model. These types can be more informative than traditional tests. To estimate parameters, we consider Bayesian approach using integrate...

Abstract The conditional extremes framework allows for event-based stochastic modeling of dependent extremes, and has recently been extended to spatial spatio-temporal settings. After standardizing the marginal distributions applying an appropriate linear normalization, certain non-stationary Gaussian processes can be used as asymptotically-motivated models process conditioned on threshold exce...

Survival models with time-varying covariates (TVCs) are widely used in the literature on credit risk prediction. However, when these endogenous, inclusion procedure has been limited to practices such as lagging variables or treating them exogenous. That leads possible biased estimators (depending strength of exogeneity assumption) and a lack prediction framework that consolidates joint evolutio...

Introduction Bayesian hierarchical models with random effects are one of the most widely used methods in modern disease mapping, as a superior alternative to standardized ratios. These models are traditionally fitted through Markov Chain Monte Carlo sampling (MCMC). Due to the nature of the hierarchical models and random effects, the convergence of MCMC is very slow and unpredictable. Recently,...

The ability to differentiate bacteria beyond the species level is essential for identifying and tracking infectious disease outbreaks and to improve our knowledge of the population genetics, epidemiology, and ecology of bacterial pathogens. Commonly used subtyping methods, such as serotyping, phage typing, ribotyping, and pulsed-field gel electrophoresis, can yield ambiguous results that are di...