Copula modeling for the estimation of measures of marker classification and predictiveness performance with survival outcomes.

The discriminative and predictive power of a continuous-valued marker for survival outcomes can be summarized using the receiver operating characteristic and predictiveness curves, respectively. In this paper, fully parametric and semi-parametric copula-based constructions of the joint model of the marker and the survival time are developed for characterizing, plotting, and analyzing both curves along with other underlying performance measures. The formulations require a copula function, a parametric specification for the margin of the marker, and either a parametric distribution or a non-parametric estimator for the margin of the time to event, to respectively characterize the fully parametric and semi-parametric joint models.

A multi-source global-local model for epidemic management.

The Effective Reproduction Number Rt provides essential information for the management of an epidemic/pandemic. Projecting Rt into the future could further assist in the management process. This article proposes a methodology based on exposure scenarios to perform such a procedure.

A flexible special case of the CSN for spatial modeling and prediction.

We introduce a parsimonious, flexible subclass of the closed-skew normal (CSN) distribution that produces valid stationary spatial models. We derive and prove some relevant properties for this subfamily; in particular, we show that it is identifiable, closed under marginalization and conditioning and that a null correlation implies independence. Based on the subclass, we propose a discrete spatial model and its continuous version.