Censored median regression models have been shown to be useful for analyzing a variety of censored survival data with the robustness property. We study sparse estimation and inference of censored median regression. The new method minimizes an inverse censoring probability weighted least absolute...

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...

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...

In survival analysis, proportional hazards model is the most commonly used and the Cox model is the most popular. These models are developed to facilitate statistical analysis frequently encountered in medical research or reliability studies. In analyzing real data sets, checking the validity of...

In survival data analysis, the proportional hazards (PH), accelerated failure time(AFT), and proportional odds (PO) models are commonly used semiparametric models forthe comparison of survivability in subjects. These models assume that the survival curvesdo not cross. However, in some clinical...

We propose a new procedure for estimating the survival function of a time-to-event random variable under arbitrary patterns of censoring. Under mild smoothness assumptions, this procedure allows a unified approach to handling different kinds of censoring, while in many cases increasing...

After treatment is found to be effective in a clinical study, attention often focuses on the effect of treatment duration on outcome. Such an analysis facilitates recommendations on the most beneficial treatment duration. In many studies, treatment duration is left to the discretion of the...

Accurate business failure prediction models would be extremely valuable to many industry sectors, particularly in financial investment and lending. The potential value of such models has been recently emphasised by the extremely costly failure of high profile businesses in both Australia and...