Bayesian Estimation for the Extended t-process Regression Models with Independent Errors
Zhanfeng Wang , Kai Li
Communications in Mathematics and Statistics ›› 2021, Vol. 9 ›› Issue (3) : 261 -272.
Bayesian Estimation for the Extended t-process Regression Models with Independent Errors
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.
Extended t-process regression / Functional data / Robustness / Monte Carlo EM algorithm
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