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.

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Communications in Mathematics and Statistics ›› 2021, Vol. 9 ›› Issue (3) : 261 -272. DOI: 10.1007/s40304-019-00192-5
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Bayesian Estimation for the Extended t-process Regression Models with Independent Errors

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Abstract

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.

Keywords

Extended t-process regression / Functional data / Robustness / Monte Carlo EM algorithm

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Zhanfeng Wang,Kai Li. Bayesian Estimation for the Extended t-process Regression Models with Independent Errors. Communications in Mathematics and Statistics, 2021, 9(3): 261-272 DOI:10.1007/s40304-019-00192-5

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Funding

Anhui Provincial Natural Science Foundation(1908085MA06)

National Natural Science Foundation of China(11471302)

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