Bayesian Estimation for the Extended t-process Regression Models with Independent Errors

被引：0
|
作者：
Zhanfeng Wang
Kai Li
机构：
[1] University of Science and Technology of China,Department of Statistics and Finance, Management School
来源：
Communications in Mathematics and Statistics | 2021年 / 9卷
关键词：
Extended ; -process regression; Functional data; Robustness; Monte Carlo EM algorithm; 62G05; 62G35;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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.
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页码：261 / 272
页数：11
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