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...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.TheCThas 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.展开更多
The extended t-process regression model is developed to robustly model functional data with outlier functional curves.This paper applies Bayesian estimation to propose an estimation procedure for the model with indepe...The extended t-process regression model is developed to robustly model functional data with outlier functional curves.This paper applies Bayesian estimation to propose an estimation procedure for the model with independent errors.A Monte Carlo EM method is built to estimate parameters involved in the model.Simulation studies and real examples show the proposed method performs well against outliers.展开更多
基金supported by National Natural Science Foundation of China(Grant No.11971457)Anhui Provincial Natural Science Foundation(Grant No.1908085MA06).
文摘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.TheCThas 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.
文摘The extended t-process regression model is developed to robustly model functional data with outlier functional curves.This paper applies Bayesian estimation to propose an estimation procedure for the model with independent errors.A Monte Carlo EM method is built to estimate parameters involved in the model.Simulation studies and real examples show the proposed method performs well against outliers.
