Composite T-Process Regression Models

Process regression models, such as Gaussian process regression model (GPR), have been widely applied to analyze kinds of functional data. This paper introduces a composite of two T-process (CT), where the first one captures the smooth global trend and the second one models local details. The CT has an advantage in the local variability compared to general T-process. Furthermore, a composite T-process regression (CTP) model is developed, based on the composite T-process. It inherits many nice properties as GPR, while it is more robust against outliers than GPR. Numerical studies including simulation and real data application show that CTP performs well in prediction.