Under most reliability model assumptions, all failures in a population are considered to come from the same distribution. Each individual failure time is assumed to provide information about the likely failure times of all other devices in the population. However, from time to time, process...

Air toxic emission factor data often contain one or more censored points below a single or multiple detection limits. Such data sets are referred to as "censored." Conventional methods used to deal with censored data sets include removing non-detects, or replacing the censored points with zero,...

Variable selection is a difficult problem in statistical model building. Identification of cost efficient diagnostic factors is very important to health researchers, but most variable selection methods do not take into account the cost of collecting data for the predictors. The trade off between...

Specifying a prior distribution for the large number of parameters in the linear statistical model is a difficult step in the Bayesian approach to the design and analysis of experiments. Here we address this difficulty by proposing the use of functional priors and then by working out important...

Data do not always obey the normality assumption, and outliers can have dramatic impacts on the quality of the least squares methods. We use Huber's loss function in developing robust methods for time-course multivariate responses. We use spline basis expansion of the time-varying regression...

El propósito de este documento es presentar el trabajo sobre la sectorización y clasificación de Holdings usando Machine Learning (en español, Aprendizaje Automático) que se ha desarrollado en la Central de Balances en el Banco de España durante el último año. Este trabajo también ha...