A0154
Title: A robust t-process regression model with independent errors
Authors: Jian Qing Shi - Southern Univesity of Science and Technology (China) [presenting]
Abstract: Gaussian process regression (GPR) model is well-known to be susceptible to outliers. Robust process regression models based on t-process or other heavy-tailed processes have been developed to address the problem. However, due to the nature of the current definition for heavy-tailed processes, the unknown process regression function and the random errors are always defined jointly and thus dependently. This definition, mainly owing to the dependence assumption involved, is not justified in many practical problems and thus limits the application of those robust approaches. It also results in a limitation of the theory of robust analysis. We will discuss a new robust process regression model enabling independent random errors and will also discuss an efficient estimation procedure. We will present an application to analyse a medical game data and show that the proposed method is robust against outliers and has a better performance in prediction compared with the existing models.
