Precise identification of the time when a change in a hospital outcome has occurred enables clinical experts to search for a potential special cause more effectively. In our paper, we develop change p
Bayesian symmetric regression offers a principled framework for modeling data characterized by heavy-tailed errors and censoring, both of which are frequently encountered in medical research. Classica
This article presents the challenges faced when applying statistical process control (SPC) in today’s evolving industrial landscape. The increased adoption by manufacturers of remote condition monitor
We develop a fully Bayesian framework for function-on-scalars regression with many predictors. The functional data response is modeled nonparametrically using unknown basis functions, which produces a
